Abstract
Machine learning is widely utilized across various scientific disciplines, with algorithms and data playing critical roles in the learning process. Proper analysis and reduction of data are crucial for achieving accurate results. In this study, our focus was on predicting the correlation between cigarette smoking and the likelihood of diabetes. We employed the Naive Bayes classifier algorithm on the Diabetes prediction dataset and conducted additional experiments using the k-NN classifier. To handle the large dataset, several adjustments were made to ensure smooth learning and satisfactory outcomes. This article presents the stages of data analysis and preparation, the classifier algorithm, and key implementation steps. Emphasis was placed on graph interpretation. The summary includes a comparison of classifiers, along with standard deviation and standard error metrics.
Keywords: Machine Learning; Naive Bayes classifier; k-NN; Diabetes prediction dataset
References
- J Chaki and M Woźniak. “A deep learning based four-fold approach to classify brain MRI: BTSCNet”. Biomedical Signal Processing and Control 85 (2023): 104902.
- S Suyanto., et al. “A new nearest neighbor-based framework for diabetes detection”. Expert Systems with Applications 199 (2022): 116857.
- A Bilal. “Diabetic retinopathy detection and classification using mixed models for a disease grading database”. IEEE Access 9 (2021): 23544-23553.
- M Woźniak, M Wieczorek and J Siłka. “BiLSTM deep neural network model for imbalanced medical data of IoT systems”. Future Generation Computer Systems 141 (2023): 489-499.
- T Le., et al. “A novel wrapper–based feature selection for early diabetes prediction enhanced with a metaheuristic”. IEEE Access 9 (2020): 7869-7884.
- P Chaudhary and P Ram. “Automatic diagnosis of different grades of diabetic retinopathy and diabetic macular edema using 2-D-FBSE-FAWT”. IEEE Transactions on Instrumentation and Measurement 71 (2022): 1-9.
- J Chaki and M Woźniak. “Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review”. Biomedical Signal Processing and Control 80 (2023): 104223.
- W Siłka., et al. “Malaria Detection Using Advanced Deep Learning Architecture”. Sensors 23.3 (2023): 1501.
- F Khademi., et al. “A weighted ensemble classifier based on WOA for classification of diabetes”. Neural Computing and Applications (2022): 1-9.
- F Haque., et al. “Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait”. Sensors 22.9 (2022): 3507.