School of Information Technology, Skyline University College, Sharjah 1797, UAE.
Network and Communication Technology Lab, Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan, Malaysia 43600, Malaysia.
Comput Intell Neurosci. 2022 May 31;2022:1051388. doi: 10.1155/2022/1051388. eCollection 2022.
Fatal diseases like cancer, dementia, and diabetes are very dangerous. This leads to fear of death if these are not diagnosed at early stages. Computer science uses biomedical studies to diagnose cancer, dementia, and diabetes. With the advancement of machine learning, there are various techniques which are accessible to predict and prognosis these diseases based on different datasets. These datasets varied (image datasets and CSV datasets) around the world. So, there is a need for some machine learning classifiers to predict cancer, dementia, and diabetes in a human. In this paper, we used a multifactorial genetic inheritance disorder dataset to predict cancer, dementia, and diabetes. Several studies used different machine learning classifiers to predict cancer, dementia, and diabetes separately with the help of different types of datasets. So, in this paper, multiclass classification proposed methodology used support vector machine (SVM) and K-nearest neighbor (KNN) machine learning techniques to predict three diseases and compared these techniques based on accuracy. Simulation results have shown that the proposed model of SVM and KNN for prediction of dementia, cancer, and diabetes from multifactorial genetic inheritance disorder achieved 92.8% and 92.5%, 92.8% and 91.2% accuracy during training and testing, respectively. So, it is observed that proposed SVM-based dementia, cancer, and diabetes from multifactorial genetic inheritance disorder prediction (MGIDP) give attractive results as compared with the proposed model of KNN. The application of the proposed model helps to prognosis and prediction of cancer, dementia, and diabetes before time and plays a vital role to minimize the death ratio around the world.
致命疾病如癌症、痴呆和糖尿病非常危险。如果这些疾病在早期阶段没有被诊断出来，就会导致对死亡的恐惧。计算机科学利用生物医学研究来诊断癌症、痴呆和糖尿病。随着机器学习的进步，有各种技术可以根据不同的数据集来预测和预后这些疾病。这些数据集在世界各地都有所不同（图像数据集和 CSV 数据集）。因此，需要一些机器学习分类器来预测人类的癌症、痴呆和糖尿病。在本文中，我们使用了多因素遗传障碍数据集来预测癌症、痴呆和糖尿病。一些研究使用不同的机器学习分类器来分别预测癌症、痴呆和糖尿病，并在不同类型的数据集的帮助下进行预测。因此，在本文中，提出了一种多类分类方法，使用支持向量机（SVM）和 K-最近邻（KNN）机器学习技术来预测三种疾病，并根据准确性对这些技术进行了比较。仿真结果表明，SVM 和 KNN 模型在多因素遗传障碍预测痴呆、癌症和糖尿病方面的准确率在训练和测试中分别达到了 92.8%和 92.5%、92.8%和 91.2%。因此，与 KNN 模型相比，基于 SVM 的多因素遗传障碍预测痴呆、癌症和糖尿病的方法（MGIDP）具有吸引力。该模型的应用有助于在发病前对癌症、痴呆和糖尿病进行预后和预测，并在全球范围内降低死亡率方面发挥着至关重要的作用。
