Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults

机器学习 随机森林 可解释性 决策树 人工智能 计算机科学 集成学习 预测建模 集合预报 决策树学习
作者
Jaime Lynn Speiser,Kathryn E. Callahan,Denise K. Houston,Jason Fanning,Thomas M. Gill,Jack M. Guralnik,Anne B. Newman,Marco Pahor,W. Jack Rejeski,Michael E. Miller
出处
期刊:The Journals of Gerontology [Oxford University Press]
卷期号:76 (4): 647-654 被引量:47
标识
DOI:10.1093/gerona/glaa138
摘要

Abstract Background Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty in understanding the complex algorithms that underlie models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. Method We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. Results Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated using data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). Conclusions Machine learning methods offer an alternative to traditional approaches for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.
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