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Machine Learning Algorithms for Diabetes Classification Within the CRISP-DM Framework
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Author(s): Ismail Lamaakal (University Mohamed I, Morocco), Bentaleb Youssef (University Mohamed I, Morocco), Yassine Maleh (Sultan Moulay Slimane University, Morocco), Ibrahim Ouahbi (University Mohamed I, Morocco)and Khalid El Makkaoui (University Mohamed I, Morocco)
Copyright: 2026
Pages: 34
Source title:
Theory, Practice, and Future Direction of Large Language Models
Source Author(s)/Editor(s): Ismail Lamaakal (University Mohammed Premier, Morocco), Yassine Maleh (Sultan Moulay Slimane University, Morocco), Khalid El Makkaoui (University Mohammed Premier, Morocco), Ibrahim Ouahbi (University Mohammed Premier, Morocco)and Ahmed Abd El-Latif (Prince Sultan University, Saudi Arabia)
DOI: 10.4018/979-8-3693-8387-2.ch005
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Abstract
This research paper explores the application of machine learning for early and accur- ate diabetes diagnosis, utilizing the CRISP-DM framework for structured analysis. We conducted a comprehensive study using the Pima Indians Diabetes database to assess five prominent machine learning algorithms: Support Vector Machine (SVM), Logistic Re- gression, Decision Tree, Naive Bayes, and Random Forest. We enhanced these algorithms' predictive capabilities through GridSearchCV optimization. Our analysis revealed the Decision Tree classifier as the top performer for diabetes diagnosis accuracy within the CRISP-DM framework, achieving impressive performance metrics: 96% recall, 94% accur- acy and F1 score, and 90% precision. Despite exploring ensemble learning, which combines insights from all models, it did not surpass the effectiveness of the standalone Decision Tree model. Given the medical context of our study, we prioritized the recall score, focusing on correctly identifying actual diabetic patients.
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