Supervised Machine Learning for Predicting Carbohydrate Malabsorptions Using Hydrogen Breath Tests

Abstract

Introduction: Carbohydrate malabsorptions symptoms include intestinal fluid retention, causing diarrhea and abdominal distention. The aim of this work is to create a machine learning model that predicts carbohydrate malabsorption using H2 measurements from lactose and fructose tolerance tests. Methods: We compare the predictive ability of popular classifiers with classifiers that are specifically designed for time series data. Our approach was implemented using sklearn and sktime Python machine learning libraries. Results: The highest predictive ability for the fructose dataset was achieved using a Random Forest Classifier (balanced accuracy = 0.91). In contrast, the highest predictive ability (balanced accuracy = 0.81) for the lactose dataset was obtained using an IndividualTDE time classifier. Conclusion: Our results indicate a high predictive ability for distinguishing between carbohydrate malabsorptions. However, the detection of SIBO is challenging but adapted time classifier models could reach higher performances compared to standard methods. Our results could establish the basis of an expert system for diagnosing carbohydrate malabsorptions and SIBO, respectively.

Keywords

• carbohydrate malabsorptions
• hydrogen breath tests
• supervised machine learning
• time classifier