Abstract
Familial hypercholesterolaemia is the most common and serious form of inherited hyperlipidaemia. It has an autosomal dominant mode of inheritance, and is characterised by severely elevated low-density lipoprotein cholesterol levels. Familial hypercholesterolaemia is an important cause of premature coronary heart disease, but is potentially treatable. However, the majority of familial hypercholesterolaemia individuals are under-diagnosed and under-treated, resulting in lost opportunities for premature coronary heart disease prevention. This study aims to assess performance of machine learning algorithms for enhancing familial hypercholesterolaemia detection within the Malaysian population. We applied three machine learning algorithms (random forest, gradient boosting and decision tree) to classify familial hypercholesterolaemia among Malaysian patients and to identify relevant features from four well-known diagnostic instruments: Simon Broome, Dutch Lipid Clinic Criteria, US Make Early Diagnosis to Prevent Early Deaths and Japanese FH Management Criteria. The performance of these classifiers was compared using various measurements for accuracy, precision, sensitivity and specificity. Our results indicated that the decision tree classifier had the best performance, with an accuracy of 99.72%, followed by the gradient boosting and random forest classifiers, with accuracies of 99.54% and 99.52%, respectively. The three classifiers with Recursive Feature Elimination method selected six common features of familial hypercholesterolaemia diagnostic criteria (family history of coronary heart disease, low-density lipoprotein cholesterol levels, presence of tendon xanthomata and/or corneal arcus, family hypercholesterolaemia, and family history of familial hypercholesterolaemia) that generate the highest accuracy in predicting familial hypercholesterolaemia. We anticipate machine learning algorithms will enhance rapid diagnosis of familial hypercholesterolaemia by providing the tools to develop a virtual screening test for familial hypercholesterolaemia.
Highlights
Familial hypercholesterolaemia (FH) is the most common and serious form of inherited hyperlipidaemia, and is characterised by severely elevated low-density lipoprotein cholesterol (LDL-C) levels
The model recorded an impressive average accuracy value of 99.72% compared with the other models
The accuracy values obtained by the decision tree model for the Dutch Lipid Clinic Criteria (DLCC) and Japanese FH Management Criteria (JFHMC) diagnostic instruments were the main contributors to its overall performance
Summary
Familial hypercholesterolaemia (FH) is the most common and serious form of inherited hyperlipidaemia, and is characterised by severely elevated low-density lipoprotein cholesterol (LDL-C) levels. It is an important cause of premature atherosclerosis and coronary heart disease (CHD), but is potentially treatable [1], [2]. The majority of FH individuals remain underdiagnosed and under-treated, resulting in lost opportunities for preventing premature CHD (pCHD). With an estimated Malaysian population of 32 million, it is projected that at least 64,000–160,000 individuals are affected, the majority of whom are likely to be undiagnosed or inadequately treated. Screening based on the lipid profile and LDL-C related measures is a reasonable alternative approach to assess the risk present, but contends with problems [6]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.