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.

胰岛素抵抗的鉴定和定量需要特定的血液检查，该检查是复杂，耗时且更具侵入性的，因此很难每天跟踪变化。随着机器学习方法的发展，无需临床过程即可进行胰岛素抵抗的鉴定。在这项工作中，使用采用机器学习方法的非侵入性技术，为甘油三酸酯和HDL-c比值的个体确定了胰岛素抵抗。18个参数用于识别目的，例如年龄，性别，腰围大小，身高等，以及这些参数的组合。在CALERIE数据集上进行了实验。属性选择系统的每个输出都通过不同的计算建模，例如逻辑回归，CART，SVM，LDA，KNN，额外的树分类器。所提出的工作通过分层交叉验证测试进行了验证。结果表明，KNN和CatBoost表现出最好的结果，准确度分别为74％和73％，方差为1％，而Bernardini等人则为66％。和Stawiski等。Farran等人则占83％。使用建议的方法，一个人可以预测胰岛素抵抗，因此可以每天使用非临床方法来追踪糖尿病的预期机会。尽管日常临床过程几乎不可能做到这一点。使用建议的方法，一个人可以预测胰岛素抵抗，因此可以每天使用非临床方法来追踪糖尿病的预期机会。尽管日常临床过程几乎不可能做到这一点。使用建议的方法，一个人可以预测胰岛素抵抗，因此可以每天使用非临床方法来追踪糖尿病的预期机会。尽管日常临床过程几乎不可能做到这一点。