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A Supervised Learning Approach to Predicting Coronary Heart Disease Complications in Type 2 Diabetes Mellitus Patients

机译:预测糖尿病2型糖尿病患者冠心病并发症的监督学习方法

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A supervised machine learning approach that incorporates Genetic Algorithms (GA) and Weighted k-Nearest Neighbours (WkNN) was applied to classify type 2 diabetes mellitus (T2DM) patients according to the presence or absence of Coronary Heart Disease (CHD) complications. The investigation was carried out by analyzing potential risk factors recorded at the Ulster Hospital in Northern Ireland. A GA initialization technique that integrates medical expert knowledge was compared with traditional data-driven GA initialization techniques. The results indicate that the incorporation of expert knowledge provides only a small improvement of CHD classification performance compared with models based on data-driven initialization techniques. This may be due to data incompleteness and noise or due to the beneficial effects of treatment, which masks the complication of CHD in the dataset. Further incorporation of expert knowledge at different levels of the GA need to be addressed to improve decision support in this domain.
机译:将遗传算法(GA)和加权K-最近邻居(WKNN)掺入遗传算法(GA)和加权K-最近邻居(WKNN)的监督机学习方法根据冠心病(CHD)并发症的存在或不存在来分类2型糖尿病(T2DM)患者。通过分析在北爱尔兰北部乌尔特医院记录的潜在风险因素进行调查。将医学专家知识集成的GA初始化技术与传统的数据驱动的GA初始化技术进行了比较。结果表明,与基于数据驱动的初始化技术的模型相比,专家知识的纳入仅限CHD分类性能的少量改进。这可能是由于数据不完整和噪音或由于治疗的有益效果,这掩盖了数据集中的CHD的复杂性。需要解决在遗漏的不同层面的专家知识,以改善该领域的决策支持。

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