Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach
Mirza Rizwan Sajid1, Noryanti Muhammad2, Roslinazairimah Zakaria1
1Centre for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia Pahang, 26300, Gambang, Kuantan, Pahang Darul Makmur, Malaysia.
Insights
Nonclinical features effectively predict cardiovascular diseases (CVDs) using machine learning (ML). Random Forest models showed superior performance, enhancing risk prediction for better healthcare outcomes.
Area of Science:
- Cardiovascular disease research
- Machine learning in healthcare
- Public health
Background:
- Cardiovascular diseases (CVDs) pose a significant global health challenge, particularly in developing nations.
- Existing risk prediction models require improvement due to increasing CVD mortality.
- There is a need for accessible, nonclinical features in CVD risk assessment.
Purpose of the Study:
- To evaluate the predictive capability of nonclinical features for cardiovascular diseases (CVDs).
- To apply advanced machine learning (ML) algorithms for improved CVD risk prediction.
- To assess the feasibility of using easily available healthcare data for CVD prediction.
Main Methods:
- A gender-matched case-control study involving 460 subjects was conducted.
- Eight nonclinical features were analyzed using four supervised machine learning (ML) algorithms.
- Models were compared against logistic regression (LR) and validated using train-test split and tenfold cross-validation.
Main Results:
- Random Forest (RF), a nonlinear ML algorithm, outperformed other models and LR.
- RF achieved an Area Under the Curve (AUC) of 0.851 (train-test split) and 0.853 (cross-validation).
- Nonclinical features demonstrated predictive capability, achieving at least 71% accuracy across models.
Conclusions:
- Nonclinical features show significant potential in enhancing cardiovascular disease risk prediction models.
- Flexible computational methodologies, like ML, can improve healthcare service delivery.
- Integrating accessible nonclinical data can lead to more effective early risk assessment.
Background:
In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms.
Methods:
A gender-matched case-control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train-test split (70:30) and tenfold cross-validation approaches.
Results:
Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train-test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation.
Conclusion:
The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services.
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