Department of Computer Engineering, Datta Meghe College of Engineering, Navi Mumbai, Pin Code: 400 708, India.
Diabetes Metab Syndr. 2022 Sep;16(9):102609. doi: 10.1016/j.dsx.2022.102609. Epub 2022 Sep 5.
Healthcare is a sensitive sector, and addressing the class imbalance in the healthcare domain is a time-consuming task for machine learning-based systems due to the vast amount of data. This study looks into the impact of socioeconomic disparities on the healthcare data of diabetic patients to make accurate disease predictions.
This study proposed a systematic approach of Closest Distance Ranking and Principal Component Analysis to deal with the unbalanced dataset. A typical machine learning technique was used to analyze the proposed approach. The data set of pregnant diabetic women is analysed for accurate detection.
The results of the case are analysed using sensitivity, which demonstrates that the minority class's lack of information makes it impossible to forecast the results. On the other hand, the unbalanced dataset was treated using the proposed technique and evaluated with the machine learning algorithm which significantly increased the performance of the system.
The performance of the machine learning-based system was significantly enhanced by the unbalanced dataset which was processed with the proposed technique and evaluated with the machine learning algorithm. For the first time, an unbalanced dataset was treated with a combination of Closest Distance Ranking and Principal Component Analysis.
医疗保健是一个敏感的领域，由于数据量庞大，基于机器学习的系统在解决医疗保健领域的类别不平衡问题方面是一项耗时的任务。本研究探讨了社会经济差异对糖尿病患者医疗保健数据的影响，以做出准确的疾病预测。
本研究提出了一种最接近距离排序和主成分分析的系统方法来处理不平衡数据集。使用一种典型的机器学习技术来分析所提出的方法。对妊娠糖尿病妇女的数据进行分析，以进行准确的检测。
使用敏感性分析了案例结果，表明少数群体缺乏信息使得无法预测结果。另一方面，使用所提出的技术处理了不平衡数据集，并使用机器学习算法进行了评估，这显著提高了系统的性能。
使用所提出的技术处理不平衡数据集，并使用机器学习算法进行评估，显著提高了基于机器学习的系统的性能。首次使用最接近距离排序和主成分分析的组合来处理不平衡数据集。
