Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

Abstract

Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has high cholesterol (hypercholesterolemia), the blood vessels are blocked by fats, and thus, circulation through the arteries becomes difficult. The heart does not receive the oxygen it needs, and the risk of heart attack increases. Nowadays, machine learning (ML) has gained special interest from physicians, medical centers and healthcare providers due to its key capabilities in health-related issues, such as risk prediction, prognosis, treatment and management of various conditions. In this article, a supervised ML methodology is outlined whose main objective is to create risk prediction tools with high efficiency for hypercholesterolemia occurrence. Specifically, a data understanding analysis is conducted to explore the features association and importance to hypercholesterolemia. These factors are utilized to train and test several ML models to find the most efficient for our purpose. For the evaluation of the ML models, precision, recall, accuracy, F-measure, and AUC metrics have been taken into consideration. The derived results highlighted Soft Voting with Rotation and Random Forest trees as base models, which achieved better performance in comparison to the other models with an AUC of 94.5%, precision of 92%, recall of 91.8%, F-measure of 91.7% and an accuracy equal to 91.75%.

Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models

“…Nowadays, medicine has a variety of modern diagnostic tests, which, in cooperation with Information technology and, especially, the fields of artificial intelligence (AI) and machine learning (ML), in the hands of cardiologists are powerful weapons for the prevention or diagnosis of coronary artery disease. ML techniques now play an important role in the early prediction of disease complications in diabetes (as classification [ 15 , 16 ] or regression tasks for continuous glucose prediction [ 17 , 18 ]), cholesterol [ 19 , 20 ], hypertension [ 21 , 22 ], chronic obstructive pulmonary disease (COPD) [ 23 ], COVID-19 [ 24 ], stroke [ 25 ], chronic kidney disease (CKD) [ 26 ], liver disease (LD) [ 27 ], sleep disorders [ 28 , 29 ], hepatitis C [ 30 ], cardiovascular diseases (CVDs) [ 31 ], lung cancer [ 32 ], and metabolic syndrome [ 33 ] etc. In particular, the long-term risk prediction of CAD will concern us in the context of this study.…”

Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction

“…Moreover, ML techniques now enable medical researchers to detect significant diseases in a more sophisticated and accurate way. In this direction, ML plays an essential role in the early prediction of disease complications in diabetes (as classification [ 23 , 24 ] or regression task for continuous glucose prediction [ 25 , 26 ]), cholesterol [ 27 ], hypertension [ 28 , 29 ], hypercholesterolemia [ 30 ], chronic obstructive pulmonary disease (COPD) [ 31 ], COVID-19 [ 32 ], stroke [ 33 ], chronic kidney disease (CKD) [ 34 ], liver disease [ 35 ], hepatitis-C [ 36 ], lung cancer [ 37 ], sleep disorders [ 38 ], metabolic syndrome [ 39 ], etc.…”

Classification Algorithms for Liver Epidemic Identification

“…Clinicians might further enhance their decisions on disease detection by integrating the findings of Intelligent automation and automated learning models with those of clinical approaches [62]. The quick identification of disease complications in diabetes has greatly benefited from the use of machine learning techniques [18][19] or regression tasks for "short-term blood glucose forecasting [20], lipid [21], high blood pressure [22], high cholesterol [23], chronic inflammatory lung disease (COPD) [24], novel coronavirus disease [25], cerebrovascular accident [26], chronic renal disease [27], pulmonary carcinoma [28], insomnia [29], coronary artery disease [30].…”