Model for Predicting the Effect of Sibutramine Therapy in Obesity

TL;DR

This paper presents a model to predict how well patients will respond to sibutramine treatment for obesity based on their individual health data.

Contribution

A data-driven model using XGBoost and Shapley valuation to personalize sibutramine therapy recommendations for obesity.

Findings

The model achieved 71% accuracy in predicting 3-month weight loss outcomes.

It improved to 80% accuracy when predicting 6-month outcomes using 3-month data.

The model outperforms random chance and BMI-only models in predicting treatment response.

Abstract

Background: The development of models predicting response to weight loss therapy using sibutramine is found in only a few cases. The objective of the work is to develop a data-driven method of personalized recommendation for obesity treatment that would predict the response to sibutramine based on the current set of patient parameters. Methods: The decision system is built on the XGBoost classification algorithm along with recursive feature selection and Shapley data valuation. Using the results of clinical trials, it was trained to estimate the probability of overcoming a weight loss threshold. The model was evaluated by the accuracy metric using the Leave-One-Out cross-validation. Results: The model for predicting response to sibutramine treatment over 3 months has an accuracy of 71%. The model for predicting outcomes at the sixth month visit based on results at 3 months has an accuracy of 80%. Conclusions: Although our developed prediction model may not exhibit high precision compared to certain benchmarks, it significantly outperforms random chance or models relying only on BMI parameters. Our model used the available range of laboratory tests, which makes it possible to use this model for routine clinical use and help doctors decide whether to prescribe sibutramine.

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