Decision tree modeling in R software to aid clinical decision making

Abstract

There is increasing excitement in the healthcare field about using behavioral data and healthcare analytics for disease risk prediction, clinical decision support, and overall improvement of personalized medicine. However, this excitement has not effectively translated to improved clinical outcomes due to knowledge gaps, a lack of behavioral risk models, and resistance to evidence-based practice. Reportedly, only 10–20% of clinical decisions are known to be evidence-based and this problem is further highlighted by the fact that the US spends more money on healthcare per person than any other nation, while still wrestling with poor health outcomes. Critics say there are inadequate technological resources and analytical education for clinicians to make behavioral data useful in the medical world. Healthcare technology innovators often neglect important aspects of the reality of integrating clinical data into electronic healthcare solutions. In this study, we developed a decision tree model using R statistical software to predict diabetes since it is among the top causes of death in the US, can be poorly managed, and provides an opportunity for improvement using analytics. This study examined behavioral data and healthcare analytics for use in clinical applications, demonstrating that health information professionals can develop behavioral risk factor prediction models to bridge the gap. Results indicated that decision trees are effective in classifying diabetes in an individual at up to 89.36% accuracy.