Application and Analysis of Machine Learning Algorithms on Pima and Early Diabetes Datasets for Diabetes Prediction

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Lakshmi H N
Atluri Vani Vathsala
Bhavik K Upadhyay
A Narasimha Rao


Diabetes is a chronic condition that strike how your body burns food for energy. Much of the food you consume is converted by your body into sugar (glucose), which is then released into your bloodstream. Your pancreas releases insulin when your blood sugar levels rise. Over the years, several scholars have sought to create reliable diabetes prediction models. Due to a lack of adequate data sets and prediction techniques, this discipline still faces many unsolved research issues, which forces researchers to apply big data analytics and ML-based methodology. Four distinct machine learning algorithms are used in the study to analyze healthcare prediction analytics and solve the issues. In this investigation, the Pima and Early detection datasets were employed. We applied the Decision Tree, MLP, Naive Bayes, and Random Forest algorithms to these datasets and evaluated the accuracy and F-Measure. The goal of this research is to develop a system that could more precisely predict a patient's risk of developing diabetes.

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How to Cite
H N, L. ., Vathsala, A. V. ., Upadhyay, B. K. ., & Rao, A. N. . (2023). Application and Analysis of Machine Learning Algorithms on Pima and Early Diabetes Datasets for Diabetes Prediction. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 28–35.


Deberneh, H.M.; Kim, I. Prediction of Type 2 Diabetes Based on Machine Learning Algorithm. Int. J. Environ. Res. Public Health 2021, 18, 3317.

Leila Ismail, “Type 2 Diabetes with Artifcial Intelligence Machine Learning: Methods and Evaluation”, Archives of Computational Methods in Engineering (2022) 29:313–333 .Akib, M.G.A., Ahmed, N., Shefat, S.N. and Nandi, D., 2022. A Comparative Analysis among Online and On-Campus Students Using Decision Tree.

Huaping Zhou, Diabetes prediction model based on an enhanced deep neural network”, EURASIP Journal on Wireless Communications and Networking (2020) 2020:148.

Raja Krishnamoorthi, “A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques”, Journal of Healthcare Engineering Volume 2022, Article ID 1684017.

Ravindra Changala, “Development of Predictive Model for Medical Domains to Predict Chronic Diseases (Diabetes) Using Machine Learning Algorithms and Classification Techniques”, ARPN Journal of Engineering and Applied Sciences, VOL. 14, NO. 6, March 2019, ISSN 1819-6608.

Reddy, A. Srinivasa. "Effective CNN-MSO method for brain tumor detection and segmentation." Materials Today: Proceedings 57 (2022): 1969-1974.

WHO.Diabetes. Available online: (accessed on 20 Aug 2022).