Leveraging machine learning for the early detection and prevention of chronic diseases, including diabetes, stroke, cancer, cardiovascular conditions, kidney failure, and hypertension, holds significant promise as emphasized by the WHO. This review systematically examines the application of machine learning techniques to predict these conditions using medical records and general health checkup data, with a focus on enhancing prediction accuracy through meticulous error minimization. Critical to this endeavor is the quality of input data, where challenges such as outlier detection, missing value imputation, feature selection, data normalization, and class imbalance pose substantial obstacles to model performance. Effective data preprocessing is thus paramount, ensuring high-quality inputs that facilitate robust model selection. Techniques explored encompass supervised learning, ensemble learning, deep learning, and reinforcement learning. Performance evaluation utilizes metrics like accuracy, recall, precision, and F1-score to gauge model efficacy. Furthermore, this study identifies open research challenges and proposes future directions to improve prediction performance via advanced preprocessing and machine learning methodologies, aiming to optimize data-driven approaches for improved healthcare outcomes.

Chronic Disease Prediction, Machine Learning, Data Preprocessing, Feature Selection, Model Evaluation, Healthcare Analytics, Medical Data, Supervised Learning, Deep Learning, Ensemble Learning, Outlier Detection, Missing Value Imputation, Data Normalization, Class Imbalance, Reinforcement Learning.

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