Review on Different Algorithms for Disease Prediction

• Author(s): Om Khedkar ; Rutuja Labhshetwar ; Jayant Khandebharad ; Vaishnavi Dhakare ; Prof. Parag Jambhulkar
• Paper ID: 1704283
• Page: 475-478
• Published Date: 22-04-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 10 April-2023

Abstract

Any health-related concern must be accurately and promptly examined to prevent and treat illness. The traditional diagnostic approach might not be sufficient in the case of a serious illness. A diagnosis that is made using machine learning (ML) can be more accurate than one made using conventional methods in the construction of a medical diagnosis system for disease prediction. Supervised machine learning (ML) algorithms have shown tremendous potential in outperforming traditional systems for illness diagnosis, aiding medical personnel in the early detection of high-risk disorders.This study discusses a number of algorithms that can be employed to identify diseases based on the patient's current symptoms.We also provide a summary of the outcomes produced by the various algorithms.

Keywords

Machine learning, Decision Tree Classifier, Random Forest Classifier, Naïve Bayes Classifier, Support Vector Machine, K-Nearest Neighbors, ID3

Citations

IRE Journals:
Om Khedkar , Rutuja Labhshetwar , Jayant Khandebharad , Vaishnavi Dhakare , Prof. Parag Jambhulkar "Review on Different Algorithms for Disease Prediction" Iconic Research And Engineering Journals Volume 6 Issue 10 2023 Page 475-478

IEEE:
Om Khedkar , Rutuja Labhshetwar , Jayant Khandebharad , Vaishnavi Dhakare , Prof. Parag Jambhulkar "Review on Different Algorithms for Disease Prediction" Iconic Research And Engineering Journals, 6(10)