Neha Titarmare, Srushti Jamnerkar, Shreya Khewale, Shiba Malkhede, Anushka Meshram, Pranjali Urkude
diabetes, machine learning algorithms, healthcare
Many different machine learning techniques are used in a range of disciplines to do predictive analytics over massive data. Despite the challenges, predictive analytics in healthcare can ultimately help practitioners make quick decisions regarding the health and treatment of patients based on enormous amounts of data. Six different machine learning algorithms are used in this research effort, which addresses predictive analytics in healthcare. A collection of patient medical records is acquired for experimental purposes, and six different machine-learning algorithms are applied to the dataset. It is discussed and contrasted how well the used algorithms perform and how accurate they are. The algorithm that is most effective for diabetes prediction can be determined by comparing the various machine learning methods employed in this study. Through the use of machine learning techniques, this initiative seeks to aid medical professionals in the early diagnosis and treatment of diabetes.
Article Details
Unique Paper ID: 158947

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 1149 - 1153
Article Preview & Download

Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 10 Issue 10

Last Date for paper submitting for March Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews