Open Access Open Access  Restricted Access Subscription Access

AI-Driven Early Detection of Diabetes: A Machine Learning Approach

Sudha Rani, Nethravathi B

Abstract


Diabetes is regarded as among the most deadly and chronic diseases and is brought on by an increase in blood sugar levels. If it is left untreated or undiagnosed, numerous consequences may arise. A patient is forced to visit a diagnostic facility and consult a physician put down to the time-consuming identification process. However, the advancements in ML methods solve this important issue. Acquiring a model that can tell a patient's likelihood of acquiring diabetes is the objective of this study. Thus, to identify diabetes early on, this experiment uses five machine learning classification algorithms: Random Forest, K-Nearest Neighbor, SVM, Decision Tree, and Logistic Regression. Tests are conducted using the Bangladesh Diabetes Dataset (BDD), which comes from the Kaggle machine learning library. Several metrics, including precision, accuracy, and recall, are used to estimate the performance of these approaches. Accuracy is computed by comparing cases that were correctly and wrongly classified. When correlated to the algorithm for logistic regression, Random Forest performs better, with a High accuracy of 96%.


Full Text:

PDF

References


Oana Virgolici, Bogdana Virgolici. Diabetes Prediction Using Machine Learning Techniques: A Brief Overview. Diabetes Complications. 2024; 8(1); 1-9. Journal of Computational Intelligence in Bioinformatics ISSN 0973-385X Volume 10, Number 1 (2017) pp. 1-8 © Research Foundation http://www.rfgindia.com.

Tushar Kanti De1, Prathipati Likhitha2, J Vamsi3, T Krishna Sai4, S Jaswanth5, N Sai Krishna Teja6, P Narasimha Raju7 Diabetes Prediction using Machine Learning March 2024 International Journal of Advanced Research in Computer and Communication Engineering Vol. 13, Issue 3, March 2024 DOI: 10.17148/IJARCCE.2024.13322.

Modak, S.K.S., Jha, V.K. Diabetes prediction model using machine learning techniques. Multimed Tools Appl 83, 38523–38549 (2023). Multimedia Tools and Applications (2024) 83:38523–38549 https://doi.org/10.1007/s11042-023-16745-4.

HosamEl-Sofany and IslamA.T.F.Taj-Eddin, SamirA.El-Seoud , OmarH.Karam, Yasser M. Abdel-Latif” A Proposed Technique Using Machine Learning for the Prediction of Diabetes Disease through a Mobile App” December 2023 Hindawi International Journal of Intelligent Systems Volume 2024, Article ID 6688934, 13 pages https://doi.org/10.1155/2024/6688934.

Isfafuzzaman Tasin, Tansin Ullah Nabil, SanjidaIslam, RiasatKhan Diabetes prediction using machine learning and explainable AI techniques 2022 DOI: 10.1049/htl2.12039.

Jingyu Xue1st,a, Fanchao Min2rd,b, Fengying Ma3nd,c* Research on Diabetes Prediction Method Based on Machine Learning 202 Journal of Physics: Conference Series 1684 (2020) 012062

IOP Publishing doi:10.1088/1742-6596/1684/1/0120620.

Mitushi Soni Dept of Computer Science and Engineering Shri G.S. Institute of Technology and Science Indore, India, Dr. Sunita Varma Dept of Information Technology Shri G.S. Institute of Technology and Science Indore, India Diabetes Prediction using Machine Learning Techniques 2020.

Aishwarya Mujumdara, Dr. Vaidehi Vb Diabetes Prediction using Machine Learning Algorithms 2019 DOI:10.1109/ICACCS51430.2021.9441935 Conference: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS).

M. .F. Faruque. Asaduzzaman and 1. H. Sarker, 'Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus," 2019 International Conference on Electrical, Computer 3I1d Communication Engineering (ECCE), 2019, pp. 1-4, doi: 10.1109/ECACE.2019.8679365.

M. A. Sarwar, N. Kamal, W. Hamid, and M. A. Shah, "Prediction of Diabetes Using Machine Learning Algorithms in Healthcare," 2018 24th International Conference on Automation and Computing (ICAC), 2018, pp. 1-6, doi: 1O.239 19/IC on A C.lO18.87489 92.


Refbacks

  • There are currently no refbacks.