Department of Computer Science (CS), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.
Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan.
Comput Intell Neurosci. 2022 Jul 21;2022:2650742. doi: 10.1155/2022/2650742. eCollection 2022.
A genetic disorder is a serious disease that affects a large number of individuals around the world. There are various types of genetic illnesses, however, we focus on mitochondrial and multifactorial genetic disorders for prediction. Genetic illness is caused by a number of factors, including a defective maternal or paternal gene, excessive abortions, a lack of blood cells, and low white blood cell count. For premature or teenage life development, early detection of genetic diseases is crucial. Although it is difficult to forecast genetic disorders ahead of time, this prediction is very critical since a person's life progress depends on it. Machine learning algorithms are used to diagnose genetic disorders with high accuracy utilizing datasets collected and constructed from a large number of patient medical reports. A lot of studies have been conducted recently employing genome sequencing for illness detection, but fewer studies have been presented using patient medical history. The accuracy of existing studies that use a patient's history is restricted. The internet of medical things (IoMT) based proposed model for genetic disease prediction in this article uses two separate machine learning algorithms: support vector machine (SVM) and K-Nearest Neighbor (KNN). Experimental results show that SVM has outperformed the KNN and existing prediction methods in terms of accuracy. SVM achieved an accuracy of 94.99% and 86.6% for training and testing, respectively.
遗传疾病是一种严重的疾病，影响着全球大量人群。有多种类型的遗传疾病，但我们专注于线粒体和多因素遗传疾病的预测。遗传疾病由多种因素引起，包括母体或父体基因缺陷、多次流产、血细胞缺乏和白细胞计数低。对于早产儿或青少年的生命发育，早期发现遗传疾病至关重要。尽管提前预测遗传疾病很困难，但这种预测非常关键，因为一个人的生命进程取决于此。利用从大量患者医疗报告中收集和构建的数据集，机器学习算法可以高精度地诊断遗传疾病。最近有很多研究使用基因组测序进行疾病检测，但使用患者医疗史的研究较少。使用患者病史的现有研究的准确性受到限制。本文提出了一种基于医疗物联网 (IoMT) 的遗传疾病预测模型，使用了两种独立的机器学习算法：支持向量机 (SVM) 和 K 最近邻 (KNN)。实验结果表明，SVM 在准确性方面优于 KNN 和现有预测方法。SVM 在训练和测试中分别达到了 94.99%和 86.6%的准确率。
