Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Chronic diseases such as diabetes, cardiovascular disease, stroke, kidney disease, and hypertension are among the leading causes of death worldwide. Early detection and prediction of these diseases are essential to improve patient outcomes and reduce healthcare costs. Machine learning techniques provide powerful tools for analyzing medical datasets and predicting disease risks based on patient health records. However, the performance of machine learning models largely depends on data quality and effective preprocessing methods. This study presents a machine learning-based framework for predicting chronic diseases using structured healthcare datasets. The proposed system focuses on data preprocessing techniques including handling missing values, normalization, feature selection, and class imbalance handling to improve prediction accuracy. Multiple machine learning algorithms such as Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Networks are utilized for disease prediction. Experimental results demonstrate that appropriate preprocessing combined with machine learning significantly improves prediction performance. The proposed framework can assist healthcare professionals in early diagnosis and decision-making for chronic disease management. The proposed ensemble model achieved an accuracy of 98%, outperforming individual classifiers.