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Prediction of Heart Cancer Data Using Hybrid Optimization and Machine Learning Techniques

Prediction of Heart Cancer Data Using Hybrid Optimization and Machine Learning Techniques

Kalpdrum Passi, Prayushi Patel, Chakresh Kumar Jain
Copyright: © 2021 |Volume: 3 |Issue: 1 |Pages: 17
ISSN: 2577-4794|EISSN: 2577-4808|EISBN13: 9781799863380|DOI: 10.4018/IJEACH.2021010101
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MLA

Passi, Kalpdrum, et al. "Prediction of Heart Cancer Data Using Hybrid Optimization and Machine Learning Techniques." IJEACH vol.3, no.1 2021: pp.1-17. http://doi.org/10.4018/IJEACH.2021010101

APA

Passi, K., Patel, P., & Jain, C. K. (2021). Prediction of Heart Cancer Data Using Hybrid Optimization and Machine Learning Techniques. International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH), 3(1), 1-17. http://doi.org/10.4018/IJEACH.2021010101

Chicago

Passi, Kalpdrum, Prayushi Patel, and Chakresh Kumar Jain. "Prediction of Heart Cancer Data Using Hybrid Optimization and Machine Learning Techniques," International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH) 3, no.1: 1-17. http://doi.org/10.4018/IJEACH.2021010101

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Abstract

The traditional methods of cancer diagnosis and cancer-type recognition have quite a large number of limitations in terms of speed and accuracy. However, recent studies on cancer diagnosis are focused on molecular level identification so as to improve the capability of diagnosis process. By statistically analyzing the heart cancer datasets using a set of protocols and algorithms, gene expression profiles are efficiently analyzed. Various machine learning classifiers are used to classify the selected data. Cross-validation was performed to avoid overfitting and different ratios of training, and testing data was used to conclude the best optimization technique and classification algorithm for the heart cancer datasets. The data is optimized using optimization techniques like particle swarm optimization (PSO), grey wolf optimization (GWO), and hybrid particle swarm optimization with grey wolf optimizer (HPSOGWO). Results show an improvement in the prediction accuracy of heart cancer by the hybrid algorithm as compared to PSO and GWO algorithms.

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