Multitudinous Disease Forecasting Using Extreme Learning Machine

In today's world, Machine learning and Artificial intelligence are applied to a wide range of healthcare services. Predictive modeling is implemented in this system with the basis of symptoms entered by the user, the system will perform the disease prediction. For implementing the disease prediction, it implements a Support vector, Extreme learning, Random Forest classifier, KNN and Logistic Regression for analysis. The developed system predicts Malaria, Heart, and Parkinson using Extreme Learning. The proposed system uses a KNN, Random Forest, XG Boost, Extreme Learning for predicting disease with better accuracy. There will be several parameters asked as input from the user related to the disease selected by the user. With this input, the proposed system will determine whether the user has the disease. After applying our expertise, the algorithm with the highest accuracy rate is chosen for each ailment. The extreme learning algorithm has provided an accuracy of 93% compared with other machine learning algorithms in disease prediction. This proposed system will help a lot of people for predicting multiple diseases at the same point.