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Abstract Background Hand grip strength (HGS) and pinch strength are important clinical measures for assessing the hand and overall health. Objective The aim of the present study is to predict HGS and pinch strength based on 1 hand anthropometry, and (2) body anthropometric parameters using machine learning. Methods A Secondary analysis was conducted on 542 participant aged 30–60 years from the Persian Organizational Cohort study in Mashhad University of Medical Sciences. Artificial Neural Network (ANN) were fitted as prediction model. The dataset was divided into two sets: a training set, which comprised 70% of the data, and a test set, which comprised 30% of the data. Various combinations of the hand anthropometric, demographic, and body anthropometric parameters were used to determine the most accurate model. Results The optimal HGS model, using the input of gender, body mass, and hand anthropometric parameters of length (both total length and palm), maximum width, maximum breadth, and hand shape index, achieved nearly equal accuracy to the model that incorporated all variables (RMSE = 5.23, Adjusted R2 = 0.67). As for pinch strength, gender, hand length (both total length and palm), maximum width, maximum breadth, hand shape index, hand span, and middle finger length came closest to the model incorporating all variables (RMSE = 1.20, Adjusted R2 = 0.52). Conclusion This ANN model showed that hand anthropometric parameters of total length, palm length, maximum width, maximum breadth, and the hand shape index, emerge as optimal predictors for both HGS and HPS. Body anthropometric factors (e.g., body mass) play roles as predictors for HGS, whereas their influence on pinch strength appears to be less pronounced. Level of evidence Level III (Diagnosis). Trial registration Not applicable. |
