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.
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Jędrzejczyk, J., Maliniecki, B., Woźnicka, A. (2024). Predicting Diabetes Risk in Correlation with Cigarette Smoking. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2023. Communications in Computer and Information Science, vol 1979. Springer, Cham. https://doi.org/10.1007/978-3-031-48981-5_24
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DOI: https://doi.org/10.1007/978-3-031-48981-5_24
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