Predicting Disease Risk with Machine Learning: A Comparative Study of Classification Algorithms

The objective of this research article is to enhance health outcomes by developing a model that predicts illnesses based on patient symptoms by conducting a comparative analysis of classification algorithms for disease risk prediction using machine learning techniques. Preprocessing and analyzing data is part of the project, which looks for relationships between symptoms and illnesses using patient symptom reports and electronic health information. In order to reliably detect diseases or health problems from reported symptoms, the prediction model uses machine learning techniques including decision trees, random forests, and neural networks. Since machine learning may provide light on illness trends, treatment efficacy, and patient outcomes, it is a vital tool in the healthcare industry. It's useful for illness diagnosis and prediction because of its speedy and accurate huge dataset analysis capabilities. Better patient outcomes can be achieved by using machine learning to anticipate diseases more accurately. This can help healthcare practitioners make well-informed decisions and create individualized treatment programs. With the goal of enhancing patient and healthcare system outcomes, this research attempts to shed light on the feasibility and precision of using machine learning to predict diseases based on symptoms. This research may pave the way for the creation of new instruments and technologies that enhance disease detection and treatment.