Objective Features Extracted from Motor Activity Time Series for Food Addiction Analysis Using Machine Learning

Autor:	Borisenkov, Mikhail, Velichko, Andrei, Belyaev, Maksim, Korzun, Dmitry, Tserne, Tatyana, Bakutova, Larisa, Gubin, Denis
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Signal Processing Physics - Medical Physics
Druh dokumentu:	Working Paper
Popis:	This study investigates machine learning algorithms to identify objective features for diagnosing food addiction (FA) and assessing confirmed symptoms (SC). Data were collected from 81 participants (mean age: 21.5 years, range: 18-61 years, women: 77.8%) whose FA and SC were measured using the Yale Food Addiction Scale (YFAS). Participants provided demographic and anthropometric data, completed the YFAS, the Zung Self-Rating Depression Scale, and the Dutch Eating Behavior Questionnaire, and wore an actimeter on the non-dominant wrist for a week to record motor activity. Analysis of the actimetric data identified significant statistical and entropy-based features that accurately predicted FA and SC using ML. The Matthews correlation coefficient (MCC) was the primary metric. Activity-related features were more effective for FA prediction (MCC=0.88) than rest-related features (MCC=0.68). For SC, activity segments yielded MCC=0.47, rest segments MCC=0.38, and their combination MCC=0.51. Significant correlations were also found between actimetric features related to FA, emotional, and restrained eating behaviors, supporting the model's validity. Our results support the concept of a human bionic suite composed of IoT devices and ML sensors, which implements health digital assistance with real-time monitoring and analysis of physiological indicators related to FA and SC. Comment: 16 pages, 3 figures, 14 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.00310 Zobrazit plný text záznamu View this record from Arxiv