Diseases Predictor using Machine Learning

For disease management and treatment to be successful in the healthcare industry, prompt and correct diagnosis is crucial. The potential for creating intelligent disease prediction systems has greatly increased with the use of machine learning techniques. This study describes Project Ailment Analysis, a Python-based disease prediction program that makes use of the Naive Bayes, Decision Tree, and Random Forest machine learning methods. This project's primary objective is to identify the most likely illness from the patient's records and symptoms. The system seeks to optimize the diagnosis process and assist medical practitioners in making well-informed decisions by leveraging the power of these algorithms. The article discusses the rationale behind the project, presents a literature review of related work, describes the objectives, the proposed approach, and the implemented algorithms. Furthermore, the methodology, implementation details and future scope of the project are discussed and concluded with a summary of the research findings.