Open AccessDisease Prediction Using Machine Learning
Author(s) -
Gaurav Shilimkar,
Amol Bhilare,
Shivam Pisal
Publication year - 2021
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/ijsrst12183118
Subject(s) - computer science , decision tree , cluster analysis , imperfect , association rule learning , machine learning , big data , data mining , set (abstract data type) , artificial intelligence , tree (set theory) , data set , decision tree learning , mathematical analysis , philosophy , linguistics , mathematics , programming language
Big data has a significant part in a number of businesses, but it is largely essential to the rapidly growing healthcare industry. It plays an important role by offering a large set of data points, constructing a robust system which allows for better and more accurate results in disease detection. Originally, the forecasts are made on the information accessible, but the absence of imperfect information contributes to a decrease in the caliber of precision. Besides incomplete data different qualities of particular regional diseases, which change based on their areas of origin can weaken the prediction models further. In this paper we use data mining techniques such as association rule mining, classification, clustering and finally the Decision Tree Machine learning algorithm to analyze the different kinds of general body-based illnesses. We implemented and assessed the efficacy of the Decision Tree algorithm over real-life clinical information.