An effective heuristic for developing hybrid feature selection in high dimensional and low sample size datasets.

Background: High-dimensional datasets with low sample sizes (HDLSS) are pivotal in the fields of biology and bioinformatics. One of core objective of HDLSS is to select most informative features and discarding redundant or irrelevant features. This is particularly crucial in bioinformatics, where accurate feature (gene) selection can lead to breakthroughs in drug development and provide insights into disease diagnostics.

Robust metabolic syndrome risk score based on triangular areal similarity.

One of the limitations of currently-used metabolic syndrome (MetS) risk calculations is that they often depend on sample characteristics. To address this, we introduced a novel sample-independent risk quantification method called 'triangular areal similarity' (TAS) that employs three-axis radar charts constructed from five MetS factors in order to assess the similarity between standard diagnostic thresholds and individual patient measurements. The method was evaluated using large datasets of Korean ( = 72,332) and American ( = 11,286) demographics further segmented by sex, age, and race.

Machine learning-based predictive model for prevention of metabolic syndrome.

Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and manage it effectively if they can easily monitor their health status in their daily lives.