A Novel Intelligent Frame Work for Predicting Heart Diseases Using Machine Learning

Authors

  • Neha Madame  Research Scholar (M.Tech) Department of CSE, Bhabha Engineering Research Institute, Bhabha, University Bhopal, Madhya Pradesh, India
  • Prof. Mashhood Siddiqui  Department of CSE, Bhabha Engineering Research Institute, Bhabha, University Bhopal, Madhya Pradesh, India

Keywords:

Heart Diseases, PSO (Particle Swarm optimization), ACO (Ant colony optimization)

Abstract

Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. the diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity and execution time. In this paper, we exploited the Modified Fast Correlation-Based Feature Selection (FCBF) method to filter redundant features in order to improve the quality of heart disease classification. Then, we perform a classification based on different classification algorithms such as K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Random Forest and a Multilayer Perception Artificial Neural Network optimized by Modified Particle Swarm Optimization (MPSO) combined with Ant Colony Optimization (ACO) approaches. The proposed mixed approach is applied to heart disease dataset; the results demonstrate the efficacy and robustness of the proposed hybrid method in processing various types of data for heart disease classification. Therefore, this study examines the different machine learning algorithms and compares the results using different performance measures, i.e. accuracy, precision, recall, f1-score, etc. A maximum classification accuracy of 99.65% using the optimized model proposed by FCBF, PSO and ACO. The results show that the performance of the proposed system is superior to that of the classification technique presented above.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-10-30

Issue

Volume 8, Issue 5, September-October-2022

Section

Research Articles

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How to Cite

[1]
Neha Madame, Prof. Mashhood Siddiqui, " A Novel Intelligent Frame Work for Predicting Heart Diseases Using Machine Learning " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 5, pp.72-85, September-October-2022.