An Automation for Multiple Diseases Prediction and Consultation Using Efficient Machine Learning Algorithms

Healthcare systems worldwide face mounting pressure to deliver accurate, timely diagnostic support while managing increasing patient volumes.This research presents a comprehensive automated framework for simultaneous prediction of three critical health conditions: cardiovascular disorders, pulmonary carcinoma, and diabetes mellitus.The proposed system is using machine learning techniques with practical software implementation using C# programming language and K-Nearest Neighbors (KNN) classification technique and for front end we will using visual studio and microsoft Tools and backend we using Sql.The developed platform processes diverse patient parameters including biochemical markers, physiological measurements, and demographic characteristics to generate predictive assessments.Our implementation leverages the KNN algorithm's pattern recognition capabilities to identify disease signatures within historical patient datasets.The system architecture encompasses data preprocessing modules, feature selection mechanisms, and real-time consultation interfaces designed for clinical environments.proposedsystem will have the following enhanced features.Building real time browser based application useful for doctors and medical departments.Developing a generic application useful for multiple hospitals.Real time datasets used for multiple diseases prediction.Programming the algorithm for the real time datasets, we use KNN algorithm to analyze medical datasets.Writing the algorithm program to work with dynamic datasets.