Early Detection and Prevention of Chronic Diseases: A Comprehensive Review

Chronic disease early prevention is of vital importance in enhancing patient outcomes, healthcare expenses, and proactive clinical interventions. As the chronic disorders like diabetes, cardiovascular disorders, cancer and neurological disorders continue to rise, the traditional modes of diagnosis may not always give a solution in real-time and at a large scale. Recent improvements in machine learning have tremendously improved the ability of healthcare systems to identify chronic diseases at an early stage. This paper will provide an overview of the current practices to detect chronic diseases in early stages, specifically based on clinical data, electronic health records, medical radiographs, wearable sensors, and genome data. The systematic review presents classical statistical models and contemporary machine learning methods, which are Random Forest, Support Vector Machines, Gradient Boosting, and neural networks as disease risk prediction and classification. Moreover, Convolutional Neural Networks are also discussed as potential deep learning architectures. The major issues are heterogeneity of data, imbalance in classes, privacy, interpretability of the model and model generalization to varied population, which are critically evaluated. This review has a purpose to ensure that researchers and healthcare practitioners gain a systematic insight into the existing methods and novel tendencies in chronic disease detection using ML.