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Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate
Agostino Accardo1, Luca Restivo2, Miloš Ajčević3
1Department Engineering and Architecture, University of Trieste, Trieste, Italy. accardo@units.it.
Insights
Heart rate variability (HRV) analysis aids early diagnosis of ischemic heart disease (IHD) and dilated cardiomyopathy (DCM). Machine learning models using HRV and clinical data accurately differentiate these conditions from healthy subjects.
Area of Science:
- Cardiology
- Biomedical Engineering
- Data Science
Background:
- Early diagnosis of ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) is clinically challenging.
- Differentiating IHD and DCM in early stages requires advanced diagnostic tools.
Purpose of the Study:
- To evaluate the diagnostic power of heart rate variability (HRV) features and clinical parameters using classification and regression tree (CART) modeling.
- To develop a predictive model for early-stage IHD and DCM diagnosis.
Main Methods:
- A study involving 263 IHD patients, 181 DCM patients, and 689 healthy subjects.
- Extraction of linear and non-linear HRV parameters from 24-hour Holter monitoring, considering total heart rate variability.
- Application of CART algorithm to build classification models using HRV and clinical data (age, sex, left ventricular ejection fraction - LVEF).
Main Results:
- Key HRV parameters identified by CART include MeanRR, SDNN, pNN50, LF, LF/HF, LFn, FD, and Beta exp.
- The most accurate model (73.3%) incorporated pNN50, FD, sex, age, and LVEF.
- The developed models demonstrated clinical interpretability and relevance for early diagnostic support.
Conclusions:
- HRV analysis combined with clinical data (age, sex, LVEF) offers a promising approach for early IHD and DCM diagnosis.
- CART modeling provides clinically interpretable insights for differentiating IHD, DCM, and healthy individuals.
- This method supports the initial diagnostic process for IHD and DCM with clinically relevant accuracy.
Abstract:
Diagnosis of etiology in early-stage ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) patients may be challenging. We aimed at investigating, by means of classification and regression tree (CART) modeling, the predictive power of heart rate variability (HRV) features together with clinical parameters to support the diagnosis in the early stage of IHD and DCM. The study included 263 IHD and 181 DCM patients, as well as 689 healthy subjects. A 24 h Holter monitoring was used and linear and non-linear HRV parameters were extracted considering both normal and ectopic beats (heart rate total variability signal). We used a CART algorithm to produce classification models based on HRV together with relevant clinical (age, sex, and left ventricular ejection fraction, LVEF) features. Among HRV parameters, MeanRR, SDNN, pNN50, LF, LF/HF, LFn, FD, Beta exp were selected by the CART algorithm and included in the produced models. The model based on pNN50, FD, sex, age, and LVEF features presented the highest accuracy (73.3%). The proposed approach based on HRV parameters, age, sex, and LVEF features highlighted the possibility to produce clinically interpretable models capable to differentiate IHD, DCM, and healthy subjects with accuracy which is clinically relevant in first steps of the IHD and DCM diagnostic process.
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