Abstract
BackgroundChronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy. Among them, diabetes mellitus (DM) and essential hypertension (EH) are two of the most prevalent ones. Furthermore, they can be the onset of other chronic conditions such as kidney or obstructive pulmonary diseases. The need to comprehend the factors related to such complex diseases motivates the development of interpretative and visual analysis methods, such as classification trees, which not only provide predictive models for diagnosing patients, but can also help to discover new clinical insights.ResultsIn this paper, we analyzed healthy and chronic (diabetic, hypertensive) patients associated with the University Hospital of Fuenlabrada in Spain. Each patient was classified into a single health status according to clinical risk groups (CRGs). The CRGs characterize a patient through features such as age, gender, diagnosis codes, and drug codes. Based on these features and the CRGs, we have designed classification trees to determine the most discriminative decision features among different health statuses. In particular, we propose to make use of statistical data visualizations to guide the selection of features in each node when constructing a tree. We created several classification trees to distinguish among patients with different health statuses. We analyzed their performance in terms of classification accuracy, and drew clinical conclusions regarding the decision features considered in each tree. As expected, healthy patients and patients with a single chronic condition were better classified than patients with comorbidities. The constructed classification trees also show that the use of antipsychotics and the diagnosis of chronic airway obstruction are relevant for classifying patients with more than one chronic condition, in conjunction with the usual DM and/or EH diagnoses.ConclusionsWe propose a methodology for constructing classification trees in a visually guided manner. The approach allows clinicians to progressively select the decision features at each of the tree nodes. The process is guided by exploratory data analysis visualizations, which may provide new insights and unexpected clinical information.
Highlights
Chronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy
The diagnoses collected by the hospital were coded according to the International Classification of Diseases - 9th revision-Clinical Modification (ICD9-CM) [28], whereas the diagnoses associated with primary care centers were coded in accordance with the International Classification of Primary Care (ICPC)
This paper focuses on classification trees, which are easier to interpret than the majority of statistical learning models
Summary
Chronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy. Diabetes mellitus (DM) and essential hypertension (EH) are two of the most prevalent ones. They can be the onset of other chronic conditions such as kidney or obstructive pulmonary diseases. The increase in life expectancy in developed countries is originating multiple chronic conditions, which may appear both independently and jointly in individuals. Their analysis is important for several reasons [1]: (1) they account for 60% of global deaths; and (2) they trigger 75% of public health expenditure (for instance, 80% of primary care consultations, and 60% of hospital admissions, are related to chronic diseases). The consumption of antihypertensive drugs may increase the probability of suffering from DM [6]
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