A Non-Invasive Approach for Total Cholesterol Level Prediction Using Machine Learning

Abstract

Artificial intelligence techniques have been increasingly applied in healthcare to help in many areas, from assisting clinical diagnoses to preventing diseases. In this paper, a machine learning approach to predict cholesterol levels using non-invasive and easy-to-collect data is presented. Specifically, it uses clinical and anthropometric data gathered by nutritionists during weight loss intervention (dieting) periods. The prediction power analysis of different patient variables is aimed at improving both non-invasive diagnosis quality and screening of associated diseases. Moreover, a clustering analysis has been carried out to identify different groupings of patients that might share some characteristics that have so far remained inconspicuous but might contain a valuable diagnosis or prognosis information for clinical experts. The experiments show a mean absolute percentage error rate (MAPE) of 4.39% in cholesterol estimation via regression, as well as clustering of patients within four profiles in which variable values share commonalities among cluster members.

Keywords

• Digital health assessment
• 3D body reconstruction
• clinical data regression
• patient data clustering
• pattern recognition