Smart System for Disease Prognostication in Healthcare using multi-classifier-based Prediction Model

Abstract

Abstract: In today's world, machine learning and Artificial Intelligence are playing a crucial role. We can find use cases of ML and AI everywhere. Starting from self-driving cars like Autopilot-Tesla to fields like medical, AI and ML having many advances. There are approaches which are generally helpful for healthcare and biomedical sectors for predicting diseases. The proposed work is to develop and deploy such a model for prognostication of diseases like Diabetes, Chronic, Heart, Liver, Malaria, Pneumonia. The proposed model is divided into 3 phases: 1) Data Normalization 2) Feature Extraction and (c) Prediction. For the process of model building, the datasets are taken from Kaggle datasets and UCI ML repository. By following all the three phases, to make the attribute's range at a certain level, normalization of data takes place. Then, the other two phases take place. Lower accuracy of the model can be fatal for patients sometimes so here the model is designed to predict the outcomes accurately. The application of the model includes entering parameters or symptoms in the system and getting the outcome whether the result is positive or negative. This model will help people to monitor their health regularly