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In Silico Clinical Trials for Cardiovascular Disease
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Hyperparameter optimization for cardiovascular disease data-driven prognostic system.

Jayson Saputra1, Cindy Lawrencya2, Jecky Mitra Saini2

  • 1Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia. jayson@binus.ac.id.

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Summary

Predicting cardiovascular diseases (CVDs) is crucial. This study used machine learning models, finding Stochastic Gradient Descent (SGD) and Artificial Neural Networks (ANN) achieved high accuracy in CVD risk prediction.

Keywords:
Cardiovascular diseaseData miningData-driven analyticsHyperparameter optimizationOrange data mining softwarePrognostic systemUnsupervised machine learning

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Area of Science:

  • Cardiovascular disease research
  • Medical data mining
  • Machine learning in healthcare

Background:

  • Cardiovascular diseases (CVDs) are a leading cause of global mortality, necessitating improved prediction and diagnostic methods.
  • Timely prognosis, considering patient history and lifestyle, is key for CVD prevention and management.
  • Utilizing patient datasets for CVD risk factor analysis presents a significant challenge in modern medicine.

Purpose of the Study:

  • To apply data mining and unsupervised machine learning techniques for analyzing Cardiovascular Disease Prognostic datasets.
  • To evaluate the performance and classification accuracy of various machine learning models for CVD prediction.
  • To determine the optimal number of clusters within CVD patient data using clustering methods.

Main Methods:

  • Employed data mining via Orange software on a dataset of 918 adult patients (28-77 years old).
  • Utilized supervised learning algorithms including k-nearest neighbors, support vector machine, random forest, artificial neural network (ANN), naïve bayes, logistic regression, stochastic gradient descent (SGD), and AdaBoost.
  • Applied unsupervised clustering methods such as k-means, hierarchical, and density-based spatial clustering of applications with noise (DBSCAN) to identify data patterns.

Main Results:

  • Stochastic Gradient Descent (SGD) and Artificial Neural Network (ANN) models demonstrated the highest performance, achieving a classification accuracy of 0.900.
  • K-means and hierarchical clustering methods indicated that the Cardiovascular Disease Prognostic datasets could be effectively divided into two distinct clusters.
  • The study highlights the strong correlation between model accuracy and the effectiveness of CVD risk prediction.

Conclusions:

  • SGD and ANN are highly effective models for predicting cardiovascular disease risk with significant accuracy.
  • The clustering of patient data into two groups suggests potential for distinct risk stratification.
  • Accurate predictive models are essential for improving diagnostic capabilities and enabling timely preventive interventions for CVD.