Key result
Soft Voting machine learning model predicts hypercholesterolemia risk with ~95% AUC.
Why the study?
Hypercholesterolemia increases heart attack risk, and machine learning methods may provide efficient risk prediction tools for hypercholesterolemia occurrence.
Can machine learning models accurately predict the long-term risk of hypercholesterolemia?
Can machine learning models accurately predict the long-term risk of hypercholesterolemia?
Effect estimate: AUC 94.5%
Machine learning models, specifically Soft Voting with Rotation and Random Forest trees, can predict the occurrence of hypercholesterolemia with high accuracy and AUC.
May aid hypercholesterolemia risk stratification; hypothesis-generating and requires prospective validation before clinical use.
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%.
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Δρίτσας et al. (2022) studied Hypercholesterolemia. Soft Voting with Rotation and Random Forest trees vs. Other ML models was evaluated on Hypercholesterolemia occurrence risk prediction (AUC 94.5%). A Soft Voting machine learning model with Rotation and Random Forest trees predicted hypercholesterolemia risk with an AUC of 94.5% and an accuracy of 91.75%.
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