Early Disease Prediction using Ml
Amit Kumar1, Harshika Bansal2, Ayush Jaiswal3, Sovit Kumar Gupta4

1Prof. Amit Kumar, Professor, Galgotias University Greater Noida, UP, India.

2Harshika Bansal, Student, GalgotiasUniversity Greater Noida, UP, India.

3Ayush Jaiswal, Student, Galgotias University Greater Noida, UP, India.

4Sovit Kumar Gupta, Student, Galgotias University Greater Noida, UP, India. 

Manuscript received on 09 July 2023 | Revised Manuscript received on 09 November 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023 | PP: 1-4 | Volume-10 Issue-11, November 2023 | Retrieval Number: 100.1/ijaent.I96940812923 | DOI: 10.35940/ijaent.I9694.11101123

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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The approach employed in disease prediction using machine learning involves making forecasts about various diseases by utilizing symptoms provided by patients or other individuals. The supervised machine learning approaches called random forest classifier, KNN classifier, SVMs classifier are employed to forecast the disease. These algorithms are used to determine the disease’s probability. Accuratemedical data analysis helps with patient care and early disease identification as biomedical and healthcare data volumes rise. Diabetes, heart diseases are just a few of the illnesses we can forecast using linear regression and decision trees. Early detection is beneficial for determining the possibility of diabetes, heart disease.

Keywords: Machine Learning, Classifiers, Probability, Prediction, Approaches etc..
Scope of the Article: Machine Learning