The Halting Problem, first posited by Alan Turing in 1936, presents a fundamental question in computer science: can there exist a universal algorithm capable of determining whether any given program, when provided with a specific input, will eventually halt or continue to run indefinitely? Turing's groundbreaking proof demonstrated the inherent undecidability of this problem, meaning no single algorithm can resolve the halting question for all possible program-input pairs. This undecidability has profound implications for the limits of computational theory and the boundaries of algorithmic problem-solving. However, the practical necessity of ensuring program termination remains critical across various domains, particularly in developing reliable and secure software systems. In this paper, we propose an innovative and comprehensive framework that synergizes formal methods, symbolic execution, and machine learning to provide a practical approach to analyzing and predicting the halting behavior of programs. Our methodology begins with formal methods, specifically abstract interpretation, to approximate the program's behavior in a mathematically rigorous manner. By mapping concrete program states to an abstract domain, we create an over-approximation of program behavior that facilitates the detection of potential non-termination conditions. This step is crucial in handling the complexity of real-world programs, allowing us to strike a balance between computational feasibility and the precision of analysis. Next, we incorporate symbolic execution, a dynamic analysis technique that uses symbolic values in place of actual inputs to explore multiple execution paths of a program. Symbolic execution generates path conditions, logical constraints representing each possible execution path. These conditions are then solved using advanced Satisfiability Modulo Theories (SMT) solvers to determine their feasibility. By systematically exploring feasible paths, symbolic execution uncovers scenarios that might lead to infinite loops or non-termination, providing a dynamic perspective that complements the static analysis of abstract interpretation. To enhance our analysis further, we integrate machine learning models trained on a diverse dataset of programs with known termination behavior. These models extract features such as loop counts, recursion depths, and branching factors from the program code and use them to predict the likelihood of termination. Machine learning offers a data-driven approach, leveraging patterns and statistical correlations to provide probabilistic predictions about program behavior. This component of our framework adds an additional layer of analysis, using the power of modern computational techniques to guide and refine our predictions. Our integrated approach also includes innovative techniques such as counterexample-guided abstraction refinement (CEGAR) to iteratively improve the accuracy of our abstract models based on counterexamples provided by symbolic execution. Additionally, we employ feature importance analysis to interpret the contributions of different features in our machine learning models, enhancing the transparency and trustworthiness of our predictions. This paper presents a detailed evaluation of our framework through extensive experiments on a variety of programs, demonstrating its effectiveness and scalability. We highlight how our approach can detect non-termination scenarios in complex real- world applications, thereby contributing to the reliability and safety of software systems. Furthermore, we explore the implications of our findings for future research, emphasizing the potential for hybrid analysis techniques and the integration of explainable AI in program analysis. Our work advances the field of program analysis by offering a robust, scalable, and scientifically sound methodology for addressing the practical challenges posed by the Halting Problem. By combining the strengths of formal methods, symbolic execution, and machine learning, we provide a comprehensive solution that not only enhances the accuracy of termination predictions but also sets the stage for future innovations in software verification and automated debugging.
Keywords: Halting Problem; Program Analysis; Formal Methods; Abstract Interpretation; Symbolic Execution; Machine Learning; SMT Solvers; Software Verification; Program Termination; Automated Debugging; Counterexample-Guided Abstraction Refinement; Explainable AI; Feature Importance Analysis; Computational Theory

Background: Excessive Smartphone Use (ESU) is associated with mental disorders among University students in most developed countries. The aim of this study was to determine the distribution of ESU and its association with mental disorder among students of the University of Dschang.
Method: We conducted a cross-sectional study at the University of Dschang in which randomly selected students completed a survey comprised of a SAS-SV (10 items questionnaire), PHQ-7 (7 items questionnaire), PHQ-9 (9 items questionnaire), to screen for ESU, depression and anxiety respectively. Sleep quality was assessed using a three-item questionnaire. Multiple logistic regression was used to determine the association between ESU and mental disorder. Mental disorder was considered as the presence of one or more of the following disorders; depression, anxiety and/or sleep disorder.
Results: A total of 636 participants were included in this study. The prevalence of excessive smartphone use was 38.4% [95%CI: 34.6-42.3], and the prevalence of mental disorder was 35.8% [95%CI: 32.2-39.6]. The odds for mental disorder were 2.23 times greater among ESU [aOR =2.33, 5% CI: 1.58-3.16, p< 0.001] than in normal users.
Conclusion: There is a high prevalence of ESU among university students and a strong association with mental disorders. There is urgent need for interventions to control ESU among students for optimal academic performance and general wellbeing.
Limitations And Recommendations: This study was a transversal study; A longitudinal study and the use of a mental health specialist to confirm all cases of Mental health disorders could reveal better results.
Keywords: Excessive smartphone use; Mental health; Addiction; Students

Jewelry and body piercings are widely practiced cultural and fashion expressions, particularly in areas such as the ears, nose, and eyebrows. However, a significant number of individuals experience adverse reactions, including inflammation and persistent discomfort, commonly referred to as jewelry allergies. These allergic reactions are frequently triggered by exposure to metals like nickel, cobalt, and chromium, known allergens that can induce skin sensitivity. Ayurveda, an ancient Indian system of medicine, offers a holistic approach for managing such allergies. Ayurvedic treatment emphasizes the use of herbal remedies, dietary adjustments, and topical applications to alleviate inflammation and promote tissue repair. Commonly employed herbs such as *Azadirachta indica* (Neem), *Curcuma longa* (Turmeric), and *Aloe barbadensis* (Aloe Vera) are renowned for their anti-inflammatory, antimicrobial, and skin-soothing properties. Furthermore, Ayurveda advocates the use of hypoallergenic metals like gold and silver, which are traditionally recognized for their therapeutic benefits, to prevent allergic reactions.

This paper explores Ayurvedic strategies for managing jewelry-related allergies and complications arising from body piercings. It examines the therapeutic efficacy of natural remedies, preventative approaches, and the role of lifestyle and dietary interventions. The integration of Ayurvedic principles with modern insights into allergic responses offers a comprehensive framework for the effective management of jewelry-induced allergies and complications.

Artificial Intelligence (AI) and Machine Learning (ML) have transcended the realm of science fiction and have found their way into various facets of our daily lives, revolutionizing industries and creating new paradigms.
Healthcare
In healthcare, AI and ML are used to predict diseases, personalize treatment plans, and even discover new drugs. Algorithms can analyze vast datasets of medical records to identify patterns that human doctors might miss, enabling earlier diagnosis and more effective treatments.

The traditional moment distribution method is modified to analyze statically indeterminate beams and frames that consist of prismatic and tapered members. An expression of stiffness and carry over factors for tapered members is derived based on the solution of a second order differential equation for the curvature of a rectangular cross-sectional shape tapered in depth only.
The moment distribution method is based on distributing the applied moment at any joint to the members’ ends that connected to this joint. The distributed moment can be mathematically obtained from multiplying the applied moment by a distribution factor. Meanwhile, the distributed moment is carried over to the far end of the member based on a carry-over factor of 0.5. In the modified method there are two different carry over factors; one of them is less than 0.5 and the other is larger, depending on the tapering ratio. Three different applications on the portal frame with different support conditions and different columns’ shapes are presented with numerical analysis and results.
Keywords: Distribution moment; Stiffness factor; Carry over factor; Prismatic member; Tapered member

Recent advancements in research techniques have led to the integration of more comprehensive information from mixed methods of persona development. This study used a user-centered design approach to investigate the emotional support provided to children aged 3–6 during medical visits, which involved quantitative and qualitative methods, including questionnaires and in-depth interviews with the primary caregivers of children in this age group. The questionnaire was structured around four main aspects: (1) basic information; (2) reasons for the children's medical visits and parents' anxiety levels; (3) methods and cognitions in caring for the children's illness; and (4) approaches and cognitions regarding caring for the children's psychological and emotional states. One hundred fourteen valid questionnaire responses were collected, and in-depth interviews were conducted with eight pairs of 3-6-year-old children and their primary caregivers, to understand (1) common illnesses of the child, (2) the methods of handling these illnesses, (3) the medical consultation process and common issues encountered, and (4) the child's personal preferences. The results show that over 75% of the participants are raising more than two children. The common reasons for children's medical visits include preventive healthcare, respiratory diseases, vaccinations, and fever. When children fall ill, parents often experience anxiety, tension, and emotional agitation. When seeking medical assistance, the top two priorities for most parents are alleviating their children's symptoms and understanding the causes of illness. Although more than 60% of parents believe recording their children's conditions and symptoms is essential, less than 30% habitually record their children's health conditions. For medical care, the majority of parents prefer pediatric clinics. Three personas representing typical parent-child interactions during children's illnesses were developed: (1) vaccinations, (2) fever with febrile convulsions, and (3) injuries from falls. The critical aspects of emotional care for children include providing reassurance and guidance, acknowledging their emotions, using comforting tools to distract them when necessary, and delivering regular health education.
Keywords: persona; emotional care; preschool children; medical visit; mixed-methods

AI (ChatGPT 3.5) was programmed to simulate the mind of the cancer sufferer with regard to pain. Three simulations emerged from the Idea Coach feature of the Mind Genomics platform, BimiLeap.com. The first simulation allowed the user to overhear discussions among an oncologist, an analyst from an insurance company, an IT specialist in medical applications, the patient’s family, and the patient. The second simulation allowed the user to create three different mind-sets regarding post-procedure pain for the cancer sufferer: Survivor, Fighter, and Realist, respectively. The third simulation instructed the AI to create and answer questions about the mind-set, with comments from a doctor about each answer, and a prognosis over the next five years. The paper shows the ease with which AI can put a “human face” on the issue of cancer, and in turn become a tool for teaching medical students, as well as for working with cancer patients as it provides a better understanding of their condition.
Keywords: cancer patients; Mind Genomics; post-procedure pain; postoperative pain; synthesized patient mind-sets

Artificial Intelligence (AI) is a subfield of computer science concerned with developing systems and programs that have the ability to perceive information and convert this information into intelligent actions to achieve desired goals. They do this by receiving innumerable data and processing this data. Also they learn from their past to do task in well organized manner and to improve in future. Thus, AI describes computer models and programs which simulate human-level intelligence to perform cognitive functions, including solving complex problems and experience gathering.

Aims: The article describes the impact of various prognostic elements (CRP, age, duration of symptoms, operator skills, US examination) on the selection of operative method for patients with acute cholecystitis, and consequently displays the suggested preoperative scoring system.
Methods: The retrospective survey included patients operated for acute gallbladder inflammation during the period 2019-2023. Patients were divided into three groups: age (below 30, from 30-60, and above 60), CRP value (CRP<50mg/l, 50-100mg/l, CRP>100mg/l), duration of symptoms (below 3 days, 4-7 days, over 7 days), US examination (wall thickness 2mm, 4mm, more than 4mm), operator skill level (less than 50 operations a year, from 50-70 annually, over 70 annual operations). Each prognostic element was relevantly assessed, and resulted in the elaboration of preoperative scoring system for the laparoscopic cholecystectomy (LCH).
Results: During the indicated three years period 339 patients were operated; of those, 46.6% underwent the laparoscopic method, while 14.2 % needed conversion. Average CRP value in patients with LCH was 84.6mg/l, while the traditional open method indicated 125.5mg/l. Average duration of symptoms in LCH amounted to 2.7 days, and in traditional open method 4.1 days. Mean age of patients treated with laparoscopic method was younger (58.3 years), while the US examination both in LCH and the traditional open method indicated average wall thickness of 2-4 mm. Most LCH operations were performed by operators with over 70 performed annual operations. Screened patients over 60 years of age, CRP values exceeding 100mg/l, and duration of symptoms in excess of 7 days indicated significantly increased number of conversions, resulting in decisions for immediate open method cholecystectomy. Experience level of the operator significantly affected the percentage of conversions and consequent decisions for immediate open cholecystectomy (p<0.01). Each element was statistically evaluated and scored from 1-3, and the results were used in devising a scheme for preoperative classification of patients – preoperative scoring system (PSS).
Conclusion: Preoperative scoring system represents valuable reference for the operator in selecting the method of operation for patients with acute cholecystitis. Whilst any patient exceeding the PSS 11 is not suitable for LCH since there is almost 100% probability of conversion, the patients with PSS ranging from 5-9 are likely material for the LCH.

Medicine has constantly advanced over periods, considered by cultural practices, scientific discoveries, and technological advancements. From the herbal remedies of traditional medicine to the innovative techniques of modern-day advanced medicine, healthcare approaches have dramatically transformed to address a rising range of diseases. Traditional medicine, with its roots in ancient healing practices, continues to be an essential part of global healthcare, specifically in many developing countries. Meanwhile, contemporary medicine represents the basis of modern healthcare, combining scientific research, pharmaceuticals, and surgical interventions to treat and manage diseases. In recent years, advanced medicine has emerged, driven by cutting-edge technologies such as gene therapy, regenerative medicine, enzyme replacement therapy and personalized treatments. These innovations have the potential to revolutionize healthcare by offering more precise, effective, and targeted therapies. Understanding the distinctions, advancements, and approaches within each system of medicine provides a comprehensive view of how healthcare is evolving in the modern world.
Keywords: Healthcare, contemporary medicine, traditional medicine, regenerative medicine, advanced medicine, pharmaceuticals

A 37 year old educated lady with a BMI of 34 and history of (H/O) 7 days prior caesarean section, presented in ER of Indira Gandhi Memorial Hospital (IGMH), Maldives .She was comparatively well until the 2nd post operative day (POD) but then developed dyspepsia and discomfort and its gradually increasing and 5th day of operation, abdomen becomes distended with pain. Initial scanning reveals intra-abdominal mild to moderate collection with no obvious hemorrhage or luminal perforation. As hemodynamic status was static, it was treated conservatively, but for pain compile with Fever, distension and frequent hypoglycaemia came in sight thus she shifted for better management in tertiary centre (IGMH&DH) on 7th POD. Immediate base line screening reveals tender, distended abdomen , Fever, mild resp distress, severe hypoalbuminaemia with features of sepsis and intraabdominal thick collection, minimal pleural and pericardial effusion. Exploratory laparotomy through Mid midline incision performed and reveals foul smelling thick collection in multiple location of peritoneal cavity. Drainage and cleansing had been done with wide drain tube. Patient shifted in critical care unit with Albumin, antibiotics and other logistic cares. On the 10th POD, the patient was discharged with stable health parametres of life.

Introduction: The objective of this study was to observe biomedical waste management practices in health care facilities in the City of Kisangani and to identify factors associated with effective biomedical waste management.
Materials and Methods: A cross-sectional study with analytical aim was carried out among 422 professionals and managers of health care establishments in Kisangani and was conducted from June 15 to August 15, 2024 in secondary and tertiary care establishments of the city. Data collection was done through direct observations and questionnaires administered to health personnel. Frequencies and crude odds ratios (ORb) with 95% confidence intervals (CI) were calculated to assess the associations between certain factors and biomedical waste management.
Results: The results of this study show that the majority of healthcare providers have insufficient knowledge on the definition of biomedical waste management (BMWM), with 93.6% of them reporting not knowing it, and 64.5% have not been trained on MBWM. Among the trained providers, the majority (68.7%) received the training more than 36 months ago. In addition, 69.7% of providers do not apply biomedical waste sorting, and 78.2% do not have WHO recommended codified waste bags.
The infrastructure available for waste management is also inadequate, with 71.8% of providers reporting the presence of a non-functional incinerator, 71.3% indicating the presence of a functional placenta pit, ash pit and glass crusher, 69% reporting the presence of non-functional hand hygiene stations, and 77% citing the unavailability of means of transporting biomedical waste.
Finally, analysis of associated factors shows that the availability of functional incinerators (OR = 4.3; p < 0.000), placenta, ash pits and functional glass crushers (OR = 0.03; p < 0.000), means of waste transport (OR = 5.6; p < 0.000), and hand hygiene stations (OR = 0.12; p < 0.000) are strongly associated with better management of biomedical waste in health care facilities.
Conclusion: This study showed insufficient management of biomedical waste in health care facilities in the city of Kisangani. Interventions aimed at strengthening staff capacities and investing in infrastructure are necessary.
Keywords: Observational study; management; biomedical waste; health care establishments; City of Kisangani. Tshopo; DRC

Introduction: This study aims to identify factors associated with household drinking water treatment in the peri-urban area of Bumba.
Materials and methods: An analytical cross-sectional study was conducted among 422 households between July and November 2023. Data were collected via an administered questionnaire and an observation guide, while frequencies, odds ratios (OR) with their 95% confidence interval were calculated. A downward logistic regression analyzed the associated factors.
Results: The results show that only 23.9% of households practice water treatment at home. This practice is more likely (p<0.05) when water comes from an unimproved source (aOR: 0.5; 95% CI: 0.29-0.78), when households have a favorable attitude towards water treatment (aOR: 4.7; 95% CI: 2.65-8.43) and when they have good knowledge about drinking water (aOR: 2.4; 95% CI: 1.25-4.68).
Conclusion: In conclusion, household water treatment is low in Bumba and depends on source quality, household knowledge and attitudes. Promoting this practice is essential to prevent diarrheal diseases in areas with limited access to drinking water.
Keywords: Analysis; bumba; determinant; drinking water; peri-urban environment; home treatment

Individuals choose a domain for instruction and practice based on their preferences, abilities, and financial capabilities.

In the healthcare sector, each person dedicates time and effort to acquiring expertise in their specific area. Continuous knowledge improvement and skill enhancement are essential for confidently meeting job requirements. This journey of learning and development brings pleasure and worth.

In clinical practice, collaborative work with partners/collaborators, customers and their families, and other community members is critical for business stability and growth, career advancement, and personal life improvement. How they transfer their knowledge to clients, apply the principles of fair cooperation, build and extend their relationships with others, share their presence, and align with social norms depicts their life trajectory.

Introduction: Depression, anxiety, and stress (DAS) are significant affective states that substantially influence both the psychological and physiological state of an individual. Acquiring a nuanced understanding of the emotional landscape of working professionals within a community is important in enhancing societal progression. Daily, factors like age, gender, occupational roles, and marital status play a paramount role in affecting mental health parameters. Therefore, a thorough grasp of the relationship between demographic and psychological factors is essential.
Methods: This cross-sectional survey was conducted among 203 working individuals of Suryabinayak municipality. The data was collected using the DASS-42 questionnaire. All data were analyzed using SPSS and presented in tables.
Result: The findings revealed diverse levels of DAS within the participants. The mean score was found to be 9.42 for depression, 8.70 for anxiety, and 12.72 for stress among the participants. For depression levels, 123 individuals fall within the normal range, 25 mild, 34 moderate, 12 severe, and 9 extremely severe, underscoring the presence of serious cases that may require intervention. In terms of anxiety, 109 individuals fall within the normal range, 12 experience mild anxiety, 48 moderate, 14 severe, and 20 extremely severe levels, highlighting individuals facing considerable anxiety challenges. For Stress levels, 138 falls in the normal range, 25 experience mild, 26 moderate, 12 severe, and 2 extremely severe, reflecting a range of stress management needs. This study also examines the prevalence of DAS based on gender, occupation, age, and marital status, revealing important differences among these factors in detail.
Conclusion: Although the majority of people fall within the normal range of the DAS scale and don’t experience significant DAS symptoms, a considerable proportion still needs various kinds of mental health support like counseling, stress management, and self-care sessions.
Keywords: Depression; Anxiety; Stress; DASS; Gender; Occupation; Age; Marital Status; Suryabinayak; Bhaktapur

Down-Klinefelter Syndrome is an exceptionally rare chromosomal disorder that arises from the coexistence of two distinct genetic anomalies: Down syndrome (trisomy 21) and Klinefelter syndrome (47, XXY). The patient is a 3-year-old male with a rare and complex chromosomal condition resulting from mosaic Down syndrome and Klinefelter syndrome (48, XXY+21). The child has a history of congenital heart disease (CHD), which necessitated surgical intervention. Post-surgical recovery has been complicated, including significant morbidity such as a left foot amputation caused by a crossed vasoconstriction reflex. The patient underwent several investigations to elucidate the extent of their complex medical issues. A Doppler ultrasound revealed limited arterial flow in the left lower limb post-ECMO, with only partial arterial signals detected. Down-Klinefelter syndrome is a rare chromosomal abnormality that combines features of Down syndrome and Klinefelter syndrome. Documented complications include hypothyroidism, infantile spasms, gastrointestinal dysfunction, and congenital heart disease. To date, more than 70 cases of double aneuploidy have been reported in the literature.
Keywords: Patients; Down sindrome; Chromosomal; Disorder; Genetic abnormality

“Sidekick,” an original short fiction work, explores the concept of belonging through its two main characters, Birch and Aggie, who struggle to find social acceptance. Birch’s struggle is with paranoid schizophrenia. While he exhibits symptoms of delusions and experiences auditory complex perceptions, as do many with paranoid schizophrenia, unlike most, he also sees visual hallucinations. The general population might confuse his disability with dissociative identity disorder, or existing in multiple personality states. Instead, Birch seems cut off from reality, and while highly intelligent and educated, has been unable, especially in recent years, to maintain a successful life. The narrator, Aggie, is a retiree whose career was spent as an expatriate working with farming communities for a non-governmental organization (NGO) in Cambodia. She has recently returned to her home in Virginia. Her perceptions and experience are partially based on research and my observations while visiting Cambodia and my conversations with NGO employees there. The greater portion, however, is informed by my observations and experiences over the last eighteen years as a member of the expatriate community in Doha, Qatar, one of the Gulf states. Usually, expatriates are motivated by imagining a better life abroad in status, salary, and/or in opportunities to make a meaningful contribution. Aggie’s desires fit within the latter category, and as is common with expat life, her dreams met with obstacles, which require adjustment if the expat is to remain abroad. Aggie was unable to find a way to adjust. However, upon her return, she has realized that her home is no longer familiar to her, and while she has never quite fit into the fashionable society of Richmond’s West End, her outsider status has become even more pronounced. Her first-person account relays her chance encounter at a café with the much younger Birch and the tentative attempts these two lonely people make in pursuing a possible platonic friendship.
Keywords: short fiction; humor; mystery; friendship; paranoid schizophrenia; expatriate; Virginia; Cambodia

Background
The main disadvantage of conventional Bassini hernioplasty is that it involves forceful approximation of the groin tissues associated with tension on the suture line, which leads to increased postoperative pain, longer hospital stay delay in resuming regular physical activity, and higher recurrence rate. New techniques of tension free repair have many advantages, including decreased tension, reduced postoperative pain, decreased analgesic consumption, faster return to normal physical activity and lower incidence of recurrence. Tension free techniques are divided into open (Trabucco) and endoscopic (laparoscopic TAPP and TEP). This study was undertaken to investigate the advantages and disadvantages of conventional and laparoscopic hernioplasty.

Patients and methods
During the period 2018-2019, 28 patients, 24 men (85,7 %) and 4 women (14,3 %), underwent endoscopic hernia repair at our department. During the same period, the open tension-free technique was used in 423 patients, 388 men (84,9 %) and 44 women (10,2 %). The mean age of laparoscopic and open tension group was 55 and 52 years, respectively. The techniques were compared in terms of operating time, hospital stay, postoperative pain, analgesic usage, recurrence rate, return to normal physical activity, and complications. Follow up examinations were scheduled at one month, six months, one year and five years after surgery.

Results
The operating time in laparoscopic hernioplasty is significantly longer than in tension-free hernia repair, yet there is no statistically difference between the two techniques as concerns the length of hospital stay. In patients undergoing laparoscopic hernia repair, return to normal physical activity was significantly less, analgesics during the first three postoperative days, and reported significantly lower level of pain assessed by the visual analogy scale. On follow-up examination at five years after surgery one recurrence (3,6 %) was found in both groups. There was no significant difference between the groups in terms of normal physical activity.

Conclusion
Tension-free hernioplasty is the method of choice in the treatment of adult patients with groin hernia. Laparoscopic hernia repair has its advantages and drawbacks. It is considerably more expansive than the tension-free approach, but its use is fully justified in adults with hernia recurrence. 

The primary purpose of E-GAS monitoring is to ensure the functional stability of the electronic controller in relation to vehicle torque. At level 2 of the E-GAS monitoring concept, the calculation of permissible torque typically relies on formula-based models that simplify real vehicle behavior, accompanied by complex calibration to enhance accuracy. However, this approach often fails to adequately account for the diverse driving scenarios encountered by the vehicle. To address this limitation, this study proposes an algorithm for calculating permissible torque using machine learning at level 2 of the E-GAS monitoring concept. The effectiveness of the algorithm is validated through the analysis of real-world vehicle driving data, confirming its practicality and applicability.
Keywords: E-GAS Monitoring Concept; Torque Monitoring; Machine Learning; Electric Vehicle; Functional Safety; Vehicle Control Unit (VCU)

The menace of breast cancer poses a formidable challenge to global public health, particularly affecting women across diverse regions. Timely identification and precise prognosis are imperative for efficacious treatment and enhanced patient outcomes. Conventional diagnostic methods, such as mammography and biopsy, though widely employed, can be invasive and occasionally yield imprecise results. Within this context, machine learning (ML) algorithms have emerged as a promising avenue for breast cancer prediction. These algorithms demonstrate proficiency in scrutinizing extensive datasets, discerning intricate patterns, and subsequently formulating predictions based on the analyzed information. The research presented in this paper is dedicated to the formulation of a sophisticated predictive model for breast cancer utilizing ML algorithms. The dataset utilized encompasses comprehensive clinical and imaging data from patients diagnosed with breast cancer. Subsequent to the extraction of pertinent features from the dataset, rigorous preprocessing procedures will precede the training and testing phases of the ML models. The primary objective of this study is to identify the most accurate algorithm for predicting breast cancer. A comprehensive evaluation of various ML algorithms, including logistic regression, decision trees, random forests, and neural networks, will be undertaken to assess their efficacy in breast cancer prediction. Logistic regression, a statistical method adept at analyzing datasets with one or more independent variables and a binary outcome variable, will be employed in discerning crucial factors such as age, family history, and prior cancer diagnoses in predicting breast cancer. Decision trees, an alternative ML algorithm for classification tasks, leverage a hierarchical structure to classify data based on a sequence of decisions derived from input features. Random forests, an extension of decision trees, employ multiple trees to enhance model accuracy, each trained on a random subset of the dataset. Neural networks, inspired by the intricate architecture of the human brain, comprise interconnected layers of nodes processing input data to generate predictions. The learning mechanism involves adjusting the weights of inter-node connections based on training data. The evaluation of ML algorithm performance will be based on standard metrics including accuracy, precision, recall, and F1-score. These metrics serve as robust indicators of the model’s effectiveness in accurately predicting breast cancer. The identification of pivotal features contributing to breast cancer prediction within this study is anticipated to yield insights into the potential applications of ML algorithms in this domain, contributing significantly to the development of precise prediction models for breast cancer. In summary, this research endeavor, focusing on the prediction of breast cancer using ML algorithms, holds promise for enhancing both diagnosis and treatment of this debilitating condition. The creation of precise prediction models employing clinical and imaging data can empower healthcare providers to identify individuals at elevated risk promptly and initiate appropriate interventions. The outcomes of this study may play a pivotal role in advancing more effective breast cancer screening programs and ultimately improving patient outcomes.
Keywords: Breast cancer; Machine learning; Predictive model; Clinical data; Diagnosis