A Study on the Correlation Between Hand Grip and Age Using Statistical and Machine Learning Analysis

Handgrip strength (HGS) is an easy-to-use instrument for monitoring people's health status. Numerous researchers in many countries have done a study on handgrip disease or demographic data. This focused classifying aged groups referring to value using machine learning. A total of fifty-four participants had involved this study, ages ranging from 24 years 57 old. Digital Pinch Grip Analyzer been used measure the measurement three times get more accurate results. The result then recorded by Clinical Analysis Software (CAS) that built into analyzer. An independent t-test investigate significant factor age group classification. data were classified learning analysis which are Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes. overall dataset shows most suitable classification technique with average accuracy between 5 98%, specificity 0.79, sensitivity 0.9814 0.0185 mean absolute error. SVM also give lowest error compared RF consistent previous work there relationship age.