In recent years, the use of machine learning algorithms has become popular in the healthcare industry for predicting diseases. In this paper, we propose a framework for disease prediction that utilizes three popular algorithms, Decision Tree, Random Forest Tree, and Naive Bayes. We have outlined disease prediction framework utilizing different Ml Calculations. The dataset utilized had more than 230 maladies for processing. Based on the side effects, age, sexual orientation of an individual, the conclusion framework gives the yield as the disease that the person may well be enduring from. The weighted Decision Tree calculation gave the finest comes about as compared to the other calculations. The exactness of the weighted Decision Tree calculation for the forecast was 95.17%. Other algorithms i.e. Random Forest Tree and Naive Bayes also gave the exactness of 95%. If a recommendation system can be made for doctors and medicine while using review mining will save a lot of time. In this type of system, the user face problem in understanding the heterogeneous medical vocabulary as the users are laymen. User is confused because a large amount of medical information on different mediums are available. The idea behind this system is to adapt with the special requirements of the health domain related with users.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.