An effective correlation-based data modeling framework for automatic diabetes prediction using machine and deep learning techniques

BMC Bioinformatics

Research

Pawel Plawiak¹  Umamaheswararao Sanapala²  Chaitanya Kumar Marpu²  Kiran Kumar Patro²  Nagwan Abdel Samee³  Maali Alabdulhafith³  Jaya Prakash Allam⁴ 

[1] Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155, Krakow, Poland;Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100, Gliwice, Poland;Department of ECE, Aditya Institute of Technology and Management, 532201, Tekkali, AP, India;Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia;School of Computer Science and Engineering, VIT Vellore, 632014, Katpadi, Vellore, Tamil Nadu, India;

关键词: Diabetes;    Correlation;    Deep learning;    CNN;    Health care;    PIMA Indian diabetes;    Machine learning;   

DOI  :  10.1186/s12859-023-05488-6

 received in 2023-05-22, accepted in 2023-09-19,  发布年份 2023

来源: Springer

【 摘 要 】

The rising risk of diabetes, particularly in emerging countries, highlights the importance of early detection. Manual prediction can be a challenging task, leading to the need for automatic approaches. The major challenge with biomedical datasets is data scarcity. Biomedical data is often difficult to obtain in large quantities, which can limit the ability to train deep learning models effectively. Biomedical data can be noisy and inconsistent, which can make it difficult to train accurate models. To overcome the above-mentioned challenges, this work presents a new framework for data modeling that is based on correlation measures between features and can be used to process data effectively for predicting diabetes. The standard, publicly available Pima Indians Medical Diabetes (PIMA) dataset is utilized to verify the effectiveness of the proposed techniques. Experiments using the PIMA dataset showed that the proposed data modeling method improved the accuracy of machine learning models by an average of 9%, with deep convolutional neural network models achieving an accuracy of 96.13%. Overall, this study demonstrates the effectiveness of the proposed strategy in the early and reliable prediction of diabetes.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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