TechRxiv
2nd_Early_stage_Diabetec_Prediction.pdf (203.15 kB)
Download file

A Comparative Analysis of Early Stage Diabetes Prediction using Machine Learning and Deep Learning Approach

Download (203.15 kB)
preprint
posted on 29.11.2021, 05:59 by Md. Abu Rumman RefatMd. Abu Rumman Refat, Md Al Amin, Chetna Kaushal, Mst. Nilufa Yeasmin, Md Khairul IslamMd Khairul Islam
Diabetes is a disease that affects how your body processes blood sugar and is often referred to as diabetes mellitus. Insulin insufficiency and ineffective insulin use coincide when the pancreas cannot produce enough insulin or the human body cannot use the insulin that is produced. Insulin is a hormone produced by the pancreas that helps in the transport of glucose from food into cells for use as energy. The common effect of uncontrolled diabetes is hyper-glycemia, or high blood sugar, which plus other health concerns, raises serious health issues, majorly towards the nerves and blood vessels. According to 2014 statistics, people aged 18 or older had diabetes and, according to 2019 statistics, diabetes alone caused 1.5 million deaths. However, because of the rapid growth of machine learning(ML) and deep learning (DL) classification algorithms. indifferent sectors, like health science, it is now remarkably easy to detect diabetes in its early stages. In this experiment, we have conducted a comparative analysis of several ML and DL techniques for early diabetes disease prediction. Additionally, we used a diabetes dataset from the UCI repository that has 17 attributes, including class, and evaluated the performance of all proposed machine learning and deep learning classification algorithms using a variety of performance metrics. According to our experiments, the XGBoost classifier outperformed the rest of the algorithms by approximately 100.0%, while the rest of the algorithms were over 90.0% accurate.

Funding

2021 6th International Conference on Signal Processing, Computing and Control (ISPCC)

History

Email Address of Submitting Author

mdkito51@gmail.com

ORCID of Submitting Author

0000-0002-9125-9573

Submitting Author's Institution

Islamic University, Kushtia, Bangladesh

Submitting Author's Country

Bangladesh

Usage metrics

Licence

Exports