Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
PLoS One. 2024 May 16;19(5):e0300785. doi: 10.1371/journal.pone.0300785. eCollection 2024.
Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a chronic ailment that arises when the body fails to produce enough insulin or is unable to effectively use the insulin it produces. When diabetes is not properly managed, it often leads to hyperglycemia, a condition characterized by elevated blood sugar levels or impaired glucose tolerance. This can result in significant harm to various body systems, including the nerves and blood vessels. In this paper, we propose a multiclass diabetes mellitus detection and classification approach using an extremely imbalanced Laboratory of Medical City Hospital data dynamics. We also formulate a new dataset that is moderately imbalanced based on the Laboratory of Medical City Hospital data dynamics. To correctly identify the multiclass diabetes mellitus, we employ three machine learning classifiers namely support vector machine, logistic regression, and k-nearest neighbor. We also focus on dimensionality reduction (feature selection-filter, wrapper, and embedded method) to prune the unnecessary features and to scale up the classification performance. To optimize the classification performance of classifiers, we tune the model by hyperparameter optimization with 10-fold grid search cross-validation. In the case of the original extremely imbalanced dataset with 70:30 partition and support vector machine classifier, we achieved maximum accuracy of 0.964, precision of 0.968, recall of 0.964, F1-score of 0.962, Cohen kappa of 0.835, and AUC of 0.99 by using top 4 feature according to filter method. By using the top 9 features according to wrapper-based sequential feature selection, the k-nearest neighbor provides an accuracy of 0.935 and 1.0 for the other performance metrics. For our created moderately imbalanced dataset with an 80:20 partition, the SVM classifier achieves a maximum accuracy of 0.938, and 1.0 for other performance metrics. For the multiclass diabetes mellitus detection and classification, our experiments outperformed conducted research based on the Laboratory of Medical City Hospital data dynamics.
糖尿病是一种与血糖升高有关的慢性代谢性疾病，通常称为高血糖。随着时间的推移，这种疾病会对心脏、血管、眼睛、肾脏和神经造成损害。当身体无法产生足够的胰岛素或无法有效利用产生的胰岛素时，就会出现这种慢性疾病。如果糖尿病得不到妥善管理，通常会导致高血糖，即血糖水平升高或葡萄糖耐量受损的情况。这会对包括神经和血管在内的各种身体系统造成严重损害。在本文中，我们提出了一种使用医疗城医院实验室数据动力学的极度不平衡的多类糖尿病检测和分类方法。我们还根据医疗城医院实验室数据动力学制定了一个中度不平衡的新数据集。为了正确识别多类糖尿病，我们使用了三种机器学习分类器，即支持向量机、逻辑回归和 K 最近邻。我们还专注于降维（特征选择-过滤器、包装器和嵌入式方法），以修剪不必要的特征并提高分类性能。为了优化分类器的分类性能，我们通过 10 倍网格搜索交叉验证进行超参数优化来调整模型。在原始的极度不平衡数据集 70:30 分区和支持向量机分类器的情况下，我们使用过滤器方法根据前 4 个特征达到了最大的准确性 0.964、精度 0.968、召回率 0.964、F1 分数 0.962、科恩 kappa 分数 0.835 和 AUC 分数 0.99。通过使用包装器顺序特征选择的前 9 个特征，K 最近邻为其他性能指标提供了 0.935 和 1.0 的准确性。对于我们创建的中度不平衡数据集 80:20 分区，SVM 分类器达到了最大的准确性 0.938，并且对于其他性能指标都是 1.0。对于多类糖尿病的检测和分类，我们的实验优于基于医疗城医院数据动力学的已进行的研究。
