Early Diabetes Detection using Novel Hybird Machine Learning Algorithm
- DOI
- 10.2991/978-94-6463-754-0_37How to use a DOI?
- Keywords
- K-fold; decision tree; Gradient; pregnancies; accuracy
- Abstract
The early diagnosis and healthcare monitoring, is one of the major health concerns in worldwide. In order to develop precise model that classify the diabetes machine learning offers numerous algorithms. The machine learning techniques promised for detection of diabetes as fast as and also its cost effective when compared to the traditional methods. Therefore, in order to identify diabetes early, we proposed a novel hybrid technique in study that incorporates Decision Tree (DT) and Gradient Boosting Classifiers (GBC). In order to increase forecast accuracy, we incorporate new features like pregnancies, glucose, insulin, BMI, and age. The K-fold cross validation approach was used to train and test the machine learning techniques by using the Pima Indian Diabetes Dataset (PIDD). In the proposed system a novel hybrid algorithm achieved forecast accuracy rate of 90.5% and for K-fold cross validation it achieved forecast accuracy of 92%. The Decision Tree and Gradient Boost Classifiers achieved forecast accuracy rate of 78% and 80%. Also, we use precision, recall and F1-score to evaluate the algorithms performance. Those performance metrics are essential for predicting diabetes as they demonstrate the algorithms potential to accurately determine actual positive and minimize false positives. They emphasize the efficiency of algorithm in diabetes prediction in order to establish it as an essential tool for early prediction.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - B. Kalaivani AU - A. Chandrasekhar AU - P. Sangeetha AU - M. Renukadevi AU - M. Prakash PY - 2025 DA - 2025/06/30 TI - Early Diabetes Detection using Novel Hybird Machine Learning Algorithm BT - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025) PB - Atlantis Press SP - 423 EP - 433 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6463-754-0_37 DO - 10.2991/978-94-6463-754-0_37 ID - Kalaivani2025 ER -