Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
BMC Med Inform Decis Mak. 2021 Jun 21;21(1):194. doi: 10.1186/s12911-021-01527-5.
Cardiovascular disease is the leading cause of death in many countries. Physicians often diagnose cardiovascular disease based on current clinical tests and previous experience of diagnosing patients with similar symptoms. Patients who suffer from heart disease require quick diagnosis, early treatment and constant observations. To address their needs, many data mining approaches have been used in the past in diagnosing and predicting heart diseases. Previous research was also focused on identifying the significant contributing features to heart disease prediction, however, less importance was given to identifying the strength of these features.
This paper is motivated by the gap in the literature, thus proposes an algorithm that measures the strength of the significant features that contribute to heart disease prediction. The study is aimed at predicting heart disease based on the scores of significant features using Weighted Associative Rule Mining.
A set of important feature scores and rules were identified in diagnosing heart disease and cardiologists were consulted to confirm the validity of these rules. The experiments performed on the UCI open dataset, widely used for heart disease research yielded the highest confidence score of 98% in predicting heart disease.
This study managed to provide a significant contribution in computing the strength scores with significant predictors in heart disease prediction. From the evaluation results, we obtained important rules and achieved highest confidence score by utilizing the computed strength scores of significant predictors on Weighted Associative Rule Mining in predicting heart disease.
心血管疾病是许多国家的主要死因。医生通常根据当前的临床检查和以前诊断类似症状患者的经验来诊断心血管疾病。患有心脏病的患者需要快速诊断、早期治疗和持续观察。为了满足他们的需求，过去已经使用了许多数据挖掘方法来诊断和预测心脏病。以前的研究还侧重于确定对心脏病预测有贡献的显著特征，但对这些特征的强度的重视程度较低。
本文旨在弥补文献中的空白，因此提出了一种算法，用于衡量对心脏病预测有贡献的显著特征的强度。本研究旨在基于显著特征的分数来预测心脏病，使用加权关联规则挖掘。
确定了一组重要的特征分数和规则，用于诊断心脏病，并咨询了心脏病专家以确认这些规则的有效性。在 UCI 公开数据集上进行的实验广泛用于心脏病研究，在预测心脏病方面的置信度得分最高达到 98%。
本研究在计算心脏病预测中显著预测因子的强度分数方面做出了重要贡献。从评估结果中，我们获得了重要的规则，并通过利用加权关联规则挖掘中计算出的显著预测因子的强度分数，在预测心脏病方面获得了最高的置信度得分。
