Paper Title
Multi-Classifier Intelligent System for Human Disease Prediction and Analysis

Abstract
The recent rise in prevalence rates and number of patients has put pressure on the healthcare system, resulting in rising costs and reduced access to quality healthcare. Early diagnosis of diseases is an important factor in treating them effectively; therefore, it also requires expert medical opinions. To overcome these difficulties, this paper proposes an artificial intelligence-based Multi-Disease Predicting System that uses AI technology to predict initial diagnosis of various diseases based on patient symptoms. The proposed system uses machine learning algorithms like Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes for classifying different diseases. The proposed system experimentally proves to predict diseases with an accuracy of 93-94 percent, ensuring high dependabilityininitialstagediseaseprediction.Thesystemalsoprovidesan interactive interface to ensure smooth user interaction with it. The proposed system intends to overcome rising healthcare expenses and reduce pressure on medical professionals. Keywords - Human Disease Detection, KNN, SVM, Naive Bayes, Decision Tree, Random Forest, Stream lit, Machine Learning