Abstract
Type 2 diabetes is a lifelong disease that causes a substantial increase of sugar (glucose) in the blood. Nowadays, diabetes type 2 is a major public worldwide health challenge. Therefore, it is necessary to automate the process of predicting diseases. The dataset used was the “PIMA Indians Diabetes Data Set”. This dataset is imbalanced. Consequently, the authors have randomly selected 268 cases from each class to create a new balanced dataset. The objective is to analyse the impact of imbalanced data for predicting diabetes type 2. Four different machine learning methods have been applied to the original and balanced dataset. Neural network, k-nearest neighbors, Logistic Regression, and AdaBoost have been implemented with 10-fold cross-validation. Detailed information concerning the proposed model’s parameters is presented. The results recommend the use of Neural Networks for predicting diabetes type 2. This method presents 71.4% and 82.3% of accuracy for the original and balanced dataset, respectively. Furthermore, the proposed method has been compared with other studies available in the state of the art. Neural Networks presented 85.9% for AUC, 82.2% for F1-Score, 82.6% for Precision, 82.3% for Recall/sensitivity and 77.6% for specificity when applied in balanced data.
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This work was supported by the Polytechnic of Coimbra (ESTGOH). We thank Polytechnic of Coimbra (ESTGOH) for their continuous support in this work.
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Mesquita, F., Marques, G. (2021). Predicting Type 2 Diabetes Through Machine Learning: Performance Analysis in Balanced and Imbalanced Data. In: Elbiaze, H., Sabir, E., Falcone, F., Sadik, M., Lasaulce, S., Ben Othman, J. (eds) Ubiquitous Networking. UNet 2021. Lecture Notes in Computer Science(), vol 12845. Springer, Cham. https://doi.org/10.1007/978-3-030-86356-2_22
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