Artificial intelligence is a future and valuable tool for early disease recognition and support in patient condition monitoring. It can increase the reliability of the cure and decision making by developing useful systems and algorithms. Healthcare workers, especially nurses and physicians, are overworked due to a massive and unexpected increase in the number of patients during the coronavirus pandemic. In such situations, artificial intelligence techniques could be used to diagnose a patient with life-threatening illnesses. In particular, diseases that increase the risk of hospitalization and death in coronavirus patients, such as high blood pressure, heart disease and diabetes, should be diagnosed at an early stage. This article focuses on diagnosing a diabetic patient through data mining techniques. If we are able to diagnose diabetes in the early stages of the disease, we can force patients to stay home and care for their health, so the risk of being infected with the coronavirus would be reduced. The proposed method has three steps: preprocessing, feature selection and classification. Several combinations of Harmony search algorithm, genetic algorithm, and particle swarm optimization algorithm are examined with K-means for feature selection. The combinations have not examined before for diabetes diagnosis applications. K-nearest neighbor is used for classification of the diabetes dataset. Sensitivity, specificity, and accuracy have been measured to evaluate the results. The results achieved indicate that the proposed method with an accuracy of 91.65% outperformed the results of the earlier methods examined in this article.

人工智能是用于早期疾病识别和支持患者状况监测的未来有价值的工具。它可以通过开发有用的系统和算法来提高治疗和决策的可靠性。由于在冠状病毒大流行期间患者人数意外地大量增加，医护人员，尤其是护士和医生过度劳累。在这种情况下，人工智能技术可用于诊断患有危及生命的疾病的患者。特别是高血压、心脏病和糖尿病等会增加冠状病毒患者住院和死亡风险的疾病，应及早诊断。本文侧重于通过数据挖掘技术诊断糖尿病患者。如果我们能够在疾病的早期诊断出糖尿病，我们就可以迫使患者留在家中照顾他们的健康，从而降低感染冠状病毒的风险。所提出的方法包括三个步骤：预处理、特征选择和分类。使用 K-means 检查 Harmony 搜索算法、遗传算法和粒子群优化算法的几种组合以进行特征选择。这些组合以前没有检查过用于糖尿病诊断的应用。K 最近邻用于糖尿病数据集的分类。已测量灵敏度、特异性和准确性以评估结果。取得的结果表明，所提出的方法的准确率为 91.65%，优于本文中检查的早期方法的结果。