Machine learning-driven prediction model of adult body weight based on integrated bioelectrical impedance and...

Traditional body weight measurement methods face challenges, particularly in critically ill patients, necessitating the development of non-invasive and accurate alternatives. We aimed to develop an artificial intelligence (AI) system for weight prediction. A total of 1416 participants were recruited to gather data on their bioelectrical impedance analysis (BIA), whole-body infrared images, height, weight, age, and gender. The BIA-derived, image-derived, and fused AI models were developed and validated using data from 1365 participants, with training and testing ratios of 6:4, 7:3, and 8:2, respectively. The CatBoost model demonstrated superior performance compared to other models evaluated. The BIA-derived model exhibited a mean absolute error (MAE) of 2.58-2.78 kg in the training and testing sets. The image-derived model showed an MAE of 2.40-3.01 kg. Notably, the fused model exhibited the best predictive capability for body weight evaluation when compared to two models, achieving an MAE of 1.42-1.68 kg. All models revealed an exceptional correlation between predicted and actual weight values (r ≥ 0.96, P < 0.05). SHAP analysis indicated that height, gender, and BIA-derived features exhibit a stronger correlation with body weight compared to image-derived features. The fused AI model demonstrated superior and robust predictive capabilities for body weight assessment, establishing a solid foundation for clinical body weight prediction, particularly in critically ill patients. The web application of those weight prediction models is accessible online at https://frp-off.com:59815/.