首页|University of Ghana Reports Findings in Machine Learning (Predicting Adherence t o Behavior Change Support Systems Using Machine Learning: Systematic Review)
University of Ghana Reports Findings in Machine Learning (Predicting Adherence t o Behavior Change Support Systems Using Machine Learning: Systematic Review)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Accra, Ghana, by NewsR x correspondents, research stated, “There is a dearth of knowledge on reliable a dherence prediction measures in behavior change support systems (BCSSs). Existin g reviews have predominately focused on self-reporting measures of adherence.” Our news journalists obtained a quote from the research from the University of G hana, “These measures are susceptible to overestimation or underestimation of ad herence behavior. This systematic review seeks to identify and summarize trends in the use of machine learning approaches to predict adherence to BCSSs. Systema tic literature searches were conducted in the Scopus and PubMed electronic datab ases between January 2011 and August 2022. The initial search retrieved 2182 jou rnal papers, but only 11 of these papers were eligible for this review. A total of 4 categories of adherence problems in BCSSs were identified: adherence to dig ital cognitive and behavioral interventions, medication adherence, physical acti vity adherence, and diet adherence. The use of machine learning techniques for r eal-time adherence prediction in BCSSs is gaining research attention. A total of 13 unique supervised learning techniques were identified and the majority of th em were traditional machine learning techniques (eg, support vector machine). Lo ng short-term memory, multilayer perception, and ensemble learning are currently the only advanced learning techniques. Despite the heterogeneity in the feature selection approaches, most prediction models achieved good classification accur acies. This indicates that the features or predictors used were a good represent ation of the adherence problem. Using machine learning algorithms to predict the adherence behavior of a BCSS user can facilitate the reinforcement of adherence behavior.”
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