Classification of Cardiometabolic Risk in Early Middle-aged Women for Preventive Self-care Apps

Abstract

Menopause is a natural part of women's aging, but is often accompanied by an increased cardiometabolic risk (CMR), of which most women are unaware. Preventive self-care via mobile health applications (apps) is a promising way to address this issue, but research on apps for middle-aged women is limited. Further, modeling such risk is no trivial task in a non-clinical self-care context, where most biomarkers used in traditional models are unavailable. Machine learning (ML) is a potential option in this regard, but many ML approaches are effectively black box models, which leads to doubt regarding their trustworthiness. Therefore, in this paper we analyze and compare different decision tree and rule-based classification models, considered to be inherently interpretable, to assess the CMR of early middle-aged women in the context of a non-clinical self-care app. For this, we first defined a set of candidate determinants based on the feedback of potential users and domain experts. We then used data from a subset of the participants in the Study of Women's Health Across the Nation (SWAN) to compare these ML models with traditional risk score models, based on five cardiometabolic 10-year outcomes: heart attack, stroke, angina pectoris, diabetes, and metabolic syndrome.