Machine Learning-Based Approach for Early Detection and Prediction of Chronic Diseases

It is essential to identify and predict certain diseases early on in order to stop them from progressing. However, doctors frequently make mistakes when performing manual diagnoses. In order to successfully detect and predict prevalent chronic illnesses, this paper makes use of cutting-edge machine learning algorithms. The proposed system ensures accurate categorization of people with chronic diseases using CNN-style algorithms to automatically extract features and forecast disease as well as KNN for distance calculation and dataset matching. To enable a widespread disease forecast using the patient's symptoms, the system contains a complete dataset that includes disease symptoms, lifestyle characteristics, and specifics of doctor consultations. The proposed system as the hybrid of CNN and KNN performs better as compared to other various algorithms, including NB, DT, and LR, are also compared in this research.