Reviewing chronic ailments: predicting diseases with a multi-symptom approach

Abstract

The integration of machine learning (ML) techniques is now indispensable in healthcare, especially in addressing the challenges posed by chronic illnesses, which present a significant global health concern due to their unpredictable nature. This study compares ML techniques employed in the diagnosis and treatment of chronic conditions such as diabetes, liver disease, thyroid disease, breast cancer, heart disease, Alzheimer’s disease, and others. Two primary criteria guided the selection of diseases under investigation. Firstly, those extensively studied with ML methods, and secondly, those leveraging ML models to resolve issues or yield promising results. The research concludes that in real-time clinical practice, there is no universally proven method for selecting the optimal course of action due to each method’s unique advantages and disadvantages. While a hybrid technique may exhibit slightly slower speed growth, it holds the potential to enhance the accuracy and performance of a model.