An Explainable Hybrid Intelligent System for Prediction of Cardiovascular Disease
Authors
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Information Technology name of organization JIS College of Engineering, Kalyani, India. ,IN
Annwesha Banerjee Majumder -
Computer Science and Engineering, Swami Vivekananda University, Barrackpore, India. ,INSomsubhra Gupta
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Computer Science and Engineering, JIS College of Engineering, Kalyani, India. ,INDharmpal Singh
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Capgemini, Kolkata, India. ,INSourav Majumder
DOI:
https://doi.org/10.18311/jmmf/2023/34171Keywords:
Artificial Neural Network, Decision Tree, Explainable AI, Kernel SVM, K-nearest Neighbor, Local Interpretable Model Agnostic Explanation Logistic Regression, Random Forest, Shapely Value, Support Vector Machine.Abstract
Cardiovascular disease is one the major cause of death around the world. Even while medical science continues to assist efforts to save lives, qualified medical professionals are still in limited. Accurate diagnosis at the right time is crucial in cardiovascular disease cases, as patients might live a long life with the right medical care. Machine learning and artificial intelligence have a significant impact on the early and precise prediction of cardiovascular disease. In this paper a machine learning based model for cardiovascular disease prediction has been proposed applying Logistics Regression, Naïve Bayes, K-Nearest Neighbor, Support Vector machine, Kernel SVM, Decision Tree classifier, Random Forest and Artificial Neural network with model explanation using Explainable AI. Based on the precision, specificity, and sensitivity scores of each method, the most effective one has been selected. Local Interpretable Model Agnostic Explanation (LIME) and Shapely Value (SHAP) have been used for model explanation.
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