01812nas a2200193   4500000000100000000000100001008004100002100001600043700001500059700002900074700002000103700001700123700002400140700001800164245013600182856004700318520123900365022001401604       2023                            d1 aKiran Patro1 aJaya Allam1 aUmamaheswararao Sanapala1 aChaitanya Marpu1 aNagwan Samee1 aMaali Alabdulhafith1 aPawel Plawiak00aAn effective correlation-based data modeling framework for automatic diabetes prediction using machine and deep learning techniques  uhttps://doi.org/10.1186/s12859-023-05488-63 a<p>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.</p>
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