In this project, the effectiveness of various machine learning techniques has been explored using the Pima Indians Diabetes Dataset, and the performance of these techniques in predicting diabetes diagnosis has been investigated. The sample consists of 768 women of Pima Indian origin aged 21 and above. For the classification of diabetes status (diabetic or non-diabetic), five supervised machine learning algorithms were tested: Naive Bayes, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Logistic Regression. Each model was analyzed using three key evaluation metrics: accuracy, precision, and recall. Support Vector Machines yielded the highest accuracy (76.6%), balanced precision (72.09%), and balanced recall (56.36%). The study provides insights into the use of machine learning models in the diagnosis of diabetes
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Evaluating machine learning models on the Pima Indians Diabetes Dataset. Five supervised algorithms (Naive Bayes, Decision Tree, KNN, SVM, Logistic Regression) were tested for diabetes classification. SVM model performed the best with accuracy, precision, and recall. The study suggests machine learning models for diabetes diagnosis.
Merveemre/Machine-Learning-Project-with-Pima-Indians-Diabetes-Dataset
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Evaluating machine learning models on the Pima Indians Diabetes Dataset. Five supervised algorithms (Naive Bayes, Decision Tree, KNN, SVM, Logistic Regression) were tested for diabetes classification. SVM model performed the best with accuracy, precision, and recall. The study suggests machine learning models for diabetes diagnosis.
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