An aide diagnosis system based on k-means for insulin resistance assessment in eldery people from the Ecuadorian highlands

Vintimilla, Christian; Wong, Sara; Astudillo‐Salinas, Fabián; Encalada, Lorena; Severeyn, Erika

doi:10.1109/etcm.2017.8247554

Cited by 10 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some studies have used support vector machines (SVM) and decision tree to differentiate individuals with and without metabolic syndrome from variables as waist circumference, waist to height ratio, body mass index, among others [20,22]. The k-means algorithm has also been used to detect individuals with insulin resistance and overweight using as variables waist and hip circumferences [21,23].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Technique as Classifier of Impaired Body Fat Percentage

Cruz

Severeyn

Huerta

et al. 2021

Frontiers in Artificial Intelligence and Applications

Self Cite

View full text Add to dashboard Cite

Excess weight and obesity are indicators of an unhealthy or harmful accumulation of fat that can be dangerous to health. Body mass index (BMI) refers to height-to-weight radio and is often used to identify overweight and obesity in adults. Although BMI is commonly used to diagnose obesity and overweight, it is ineffective in differentiating between high muscle mass and elevated body fat mass. Body fat percentage (BF%) is one of the best predictors of obesity because it quantifies adipose tissue. The Deurenberg equation is among the indirect methods to measure BF%; it uses BMI, age, and sex as parameters to calculate the BF%. Machine learning techniques demonstrated to be a good classifier of overweight, obesity, and diseases related to insulin resistance and metabolic syndrome. This study intends to evaluate anthropometric parameters as classifiers of BF% alteration using support vector machines and the Deurenberg equation for BF% estimation. The database used consisted of 1978 individuals with 24 different anthropometric measurements. The results suggest the SVM as a suitable technique for classifying individuals with normal and abnormal BF% values. Accuracy, F1 score, PPV, NPV, and sensitivity were above 0.8. Besides, the specificity value is below 0.7, which indicates that false positives may occur. As future work, this research intends to apply neural networks as a classification technique.

show abstract

Section: Introductionmentioning

confidence: 99%