Information Systems Department, Arab Academy for Science and Technology -AASTMT, Cairo, Egypt.
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
Comput Intell Neurosci. 2022 Oct 11;2022:3078025. doi: 10.1155/2022/3078025. eCollection 2022.
Diabetes type 2 (T2DM) is a common chronic disease, increasingly leading to many complications and affecting vital organs. Hyperglycemia is the main characteristic caused by insufficient insulin secretion and poses a serious risk to human health. The objective is to construct a type-2 diabetes prediction model with high classification accuracy. Advanced machine learning and predictive model techniques are utilized to achieve cutting-edge techniques for the early diagnosis of diabetes. This paper proposes an efficient performance model to predict and classify the minority class of type-2 diabetes. The impact of oversampling and undersampling approaches to reduce the effect of an unbalanced clas has been compared to classification performance algorithms. Synthetic Minority Oversampling (SMOTE) and Tomek-links techniques are applied and examined. The outcomes were then compared to the original unbalanced dataset using an artificial neural network (ANN) predictive model. The model is compared with other state-of-the-art classifiers such as support vector machine (SVM), random forest (RF), and decision tree (DT). The tuned model had the best accuracy of 92.2%. The experimental findings clearly manifest the improvement in accuracy and evaluation metrics in terms of AUC and F1-measure using the SMOTE oversampling strategy rather than the baseline and undersampling schemes. The study recommends adopting dynamic hyperparameter optimization to further improve accuracy.
2 型糖尿病（T2DM）是一种常见的慢性疾病，越来越多地导致许多并发症，并影响重要器官。胰岛素分泌不足导致的高血糖是其主要特征，对人类健康构成严重威胁。本研究旨在构建具有高分类准确性的 2 型糖尿病预测模型。先进的机器学习和预测模型技术被用于实现糖尿病早期诊断的前沿技术。本文提出了一种高效的性能模型，用于预测和分类 2 型糖尿病的少数类别。比较了过采样和欠采样方法对减少不平衡类影响的效果与分类性能算法。应用并检验了合成少数过采样（SMOTE）和 Tomelinks 技术。然后，使用人工神经网络（ANN）预测模型将结果与原始不平衡数据集进行比较。该模型与支持向量机（SVM）、随机森林（RF）和决策树（DT）等其他最先进的分类器进行了比较。调整后的模型的准确率最高，为 92.2%。实验结果清楚地表明，与基线和欠采样方案相比，使用 SMOTE 过采样策略在 AUC 和 F1 度量方面显著提高了准确性和评估指标。研究建议采用动态超参数优化来进一步提高准确性。
