Disease Prediction and Treatment Recommendation Using Machine Learning

Abstract

Despite the availability of advanced technology and easy access to information, many people still rely on traditional methods of seeking medical treatment, such as visiting hospitals and consulting doctors for even minor symptoms. However, this approach can be time-consuming and inefficient, as patients with minor illnesses can take up valuable resources that could be better used to treat more serious cases. As a result, this research proposes a new approach to disease prediction using machine learning and symptom-based analysis. The goal is to develop a predictive model that can accurately identify potential diseases based on a patient's symptoms. This model utilizes machine learning techniques to analyze and process symptom data, allowing for quick and precise disease prediction. The study uses a large dataset of patient symptoms and medical records to train and test the model, which demonstrated high accuracy in predicting diseases. The results of this study suggest that the proposed model could be a useful tool for early diagnosis and treatment of diseases, with the potential to improve healthcare outcomes. Overall, this research represents an important contribution to the field of healthcare informatics, with possible applications in disease prevention, treatment, and management.