Big Data Mining for Heart Attack Diagnosis from Medical Records

Authors

  • Hitesh Singh Associate Professor, Department of Information Technology and M.Tech Integrated, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Bhuvana J. Associate Professor, Department of Computer Science and IT, Jain(Deemed-to-be University), Bangalore-27, India
  • Rajendra Pandey P. Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Jitendra Kumar Katariya Assistant Professor, Department of Computer Science & Application, Vivekananda Global University, Jaipur, India

Keywords:

Big Data Mining, Heart Attack Diagnosis, Medical Records, Improved Random Forest (IRF), prediction model

Abstract

Heart attacks are among the most dangerous ailments that people may develop. The key to controlling cardiovascular disease is to compare, contrast, and mine enormous volumes of data in a sequence that may be used to identify, control, and treat persistent problems, such as heart attacks. Forecasting, preventing, monitoring, and diagnosing cardiovascular diseases may be done through huge efficiency via big data analytics, which is well-known in the business sector for its useful application in regulating, comparing, and supervising enormous datasets. Big data technologies or methods used to mine massive databases for information include Hadoop, data mining, and visualization. Those fresh ideas, which have a wide range of uses, might be helpful in several industries, include medical. In this paper, we extend a big data mining pattern using a machine learning method to forecast the frequency of heart attacks from medical databases. Data preprocessing using the z- score normalization and feature extraction using Linear Discriminant Analysis (LDA) and classification using the Improved Random Forest (IRF). We generate enhanced presentation intensity with accuracy, precision, recall, and F- measure throughout the forecast model for heart disease with the IRF.

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References

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Published

11.07.2023

How to Cite

Singh, H. ., J., B. ., Pandey P., R. ., & Katariya, J. K. . (2023). Big Data Mining for Heart Attack Diagnosis from Medical Records . International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 35–41. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3018

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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