Python Based Diabetes Prediction Using Ensemble Machine Learning Techniques Using LR Algorithm and Hybrid Method

Authors

  • Pradeep Kumar G PG Student Department of Information Technology, Bharathiar University, Tamil Nadu India
  • Vadivel R Assistant Professor Department of Information Technology, Bharathiar University, Tamil Nadu, India

DOI:

https://doi.org/10.26438/ijcse/v10i5.4346

Keywords:

ML, Diabetic Predictio, SVM, DT, ND, LR, Ensemble

Abstract

The constant flood of fresh patient data is causing problems in the healthcare system. Researchers have been utilizing this data to help the healthcare industry improve its capacity to manage major diseases. They are also looking at how patients might be informed of symptoms in a timely way, therefore avoiding the serious hazards that come with them. Diabetes is one such condition that is spreading at an alarming rate these days. It may lead to a number of significant problems, such as decreased eyesight, myopia, burning extremities, renal failure, and heart failure. When blood sugar levels rise over a certain threshold, the human body is unable to manufacture enough insulin to maintain the appropriate level. As a consequence, diabetics must be educated on the need of adhering to appropriate treatment regimens. As a consequence, early diabetes diagnosis and classification are crucial. This method employs Machine Learning approaches to improve diabetes prediction accuracy. Furthermore, the trials showed that ensemble classifier models outperformed base classifier models on their own. Its results were compared to the same dataset when various classification techniques such as random forest, support vector machine, decision tree, and naive bayes were applied to it.

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Published

2022-05-31
CITATION
DOI: 10.26438/ijcse/v10i5.4346
Published: 2022-05-31

How to Cite

[1]
G. Pradeep Kumar and R. Vadivel, “Python Based Diabetes Prediction Using Ensemble Machine Learning Techniques Using LR Algorithm and Hybrid Method”, Int. J. Comp. Sci. Eng., vol. 10, no. 5, pp. 43–46, May 2022.

Issue

Vol. 10 No. 5 (2022): IJCSE May Edition

Section

Research Article

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