Multiple Disease Prediction Using Machine Learning with Chatbot and Doctor-Patient Appointment System

The paper analyzes the potential of machine learning in disease prediction and its integration into healthcare systems, offering a proactive healthcare strategy. ML-driven disease prediction combines powerful algorithms and a range of healthcare information. By carefully examining this information, machine learning models show patterns and relationships that are sometimes hard for human therapists to discover. These models could predict the chance of patients having numerous diseases, such as brain tumors, breast cancer, CKD, and heart disease, reaching beyond chronic ailments. This project contains three key components. Firstly, it contains a robust illness detection system that employs machine learning methods in Convolutional Neural Networks (CNN), Support Vector Machine(SVM) and Random Forest(RF). Our proposed approach exhibits amazing accuracy and faster convergence compared to standard machine-learning disease risk prediction techniques. It reliably predicts a spectrum of illnesses by evaluating huge datasets, exceeding existing disease risk prediction approaches. Secondly, the platform provides a chatbot that uses natural language processing (NLP) to assist with medical enquiries. Additionally, it features an efficient appointment system for live video consultations with doctors through WebRTC. In summary, this initiative represents an innovative mix of machine learning and healthcare services, delivering a holistic and proactive approach to healthcare.