A Non-invasive Approach to Identify Insulin Resistance with Triglycerides and HDL-c Ratio Using Machine learning

Identification and quantification of insulin resistance require specific blood test which is complex, time-consuming, and much more invasive, making it difficult to track the changes daily. With the advancement in machine learning approaches, identification of insulin resistance can be carried out without clinical processes. In this work, insulin resistance is identified for individuals with triglycerides and HDL-c ratio using non-invasive techniques employing machine learning approaches. Eighteen parameters are used for identification purposes like age, sex, waist size, height, etc., and combinations of these parameters. Experiments are conducted over the CALERIE dataset. Each output of the attribute selection system is modeled over distinct calculations like logistic regression, CARTs, SVM, LDA, KNN, extra trees classifier. The proposed work is validated with a stratified cross-validation test. Results show that KNN and CatBoost show the best results with an accuracy of 74% and 73% respectively and 1% variance compared to 66% with Bernardini et al. and Stawiski et al. and 83% with Farran et al. With the proposed approach an individual can predict the insulin resistance and hence prospective chances of diabetes might be tracked daily using non-clinical approaches. While the same is not practically possible with clinical processes daily.