Differential privacy for machine learning

This study examines an experiment conducted at Antalya Bilim University, Using machine learning algorithms to predict disease risk is a promising way to improve patient outcomes and early diagnosis in healthcare and predicting disease risk is a promising way to improve patient outcomes and early diagnosis in healthcare. Our results provide a high-level comparative visualization of six machine learning models using confusion matrices and their respective accuracy scores. The models evaluated are Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, and Support Vector Machine (SVM). The confusion matrices display the performance of each model by showing the breakdown of actual versus predicted classifications for four distinct classes. Each model is assessed in terms of its ability to correctly classify instances into these classes, with the diagonal of each confusion matrix representing correct predictions (true positives and true negatives). The intensity of the colors in the matrices indicates the concentration of predictions in each category. Above each matrix, the overall accuracy of the model is displayed, which quantifies the percentage of correct predictions across the entire dataset. This visual comparison highlights not only the accuracy of each algorithm but also gives deeper insights into the types of errors made (such as misclassifications between similar classes). The results suggest that while all models achieve relatively high accuracy, certain models like SVM and KNN slightly outperform others in terms of overall precision, minimizing misclassifications in specific class categories.