Medical Datasets Training and Enhancing Disease Prediction Accuracy

Data mining is an effective method that uses sophisticated tools and strategies to sift through massive databases in search of useful patterns. Its usefulness extends to many fields, including medicine. We used AdaBoost, Logistic Regression (LR), K-Nearest Neighbors (KNN), and Random Forest (RF) as predictive models in our investigation (ADaB). Evaluating utilizing methods like cross-validation and random sampling allowed us to concentrate on improving accuracy. Medical datasets were used to construct the following: Heart Disease (HD) dataset, Breast Cancer Wisconsin (BCW) dataset, and Covid-19 dataset. We set out to improve the accuracy of disease predictions and push the boundaries of medical data analysis. After completing the assessment procedure with the training data, the performance measurements showed that the highest accuracy was achieved with LR 83% for HD, KNN 97%, and LR 98% for Covid-19.