Big Data Predictive Analysis for Type-2 Diabetes Based Heart Disease Using Feature Extraction and Classification by Machine Learning Architectures

Authors

  • Arvind Kumar Pandey Assistant Professor,Department of Computer Science,Arka Jain University, Jamshedpur, Jharkhand, India.
  • Shreyanth S. Student, Data Science and Engineering, Birla Institute of Technology Pilani, Rajasthan, India
  • J. Prabhakaran 3 Associate Professor, Kalasalingam Business School, Kalasalingam Academy of Research and Education, (Deemed to be University)
  • Aniruddha Bodhankar Management, Assistant Professor, Dr.Ambedkar Institute of Management Studies & Research,Nagpur
  • Avadhesh Kumar Professor,Department of Computer Science and Engineering Galgotias University, Greater Noida, Uttar Pradesh
  • Nayani Sateesh Sr. Assistant Professor, Information Technology Department CVR College of Engineering, Ibrahimpatnam, Hyderabad

Keywords:

Machine Learning, big data, predictive analysis, type 2 diabetes, dimensionality reduction

Abstract

Machine learning (ML), a branch of AI, enables computers to learn without being explicitly programmed. ML is widely applied in the healthcare industry to forecast a variety of chronic conditions. For improved clinical paths to prevent complications and postpone the onset of diabetes, earlier diabetes prediction is essential. This research propose novel technique in type 2 diabetes based heart disease detection in big data predictive analysis using machine learning method. Input data has been collected as type 2 diabetes and processed for noise removal and dimensionality reduction. Then the processed data features has been extracted for detecting the abnormality of type 2 diabetes using regression model based linear discriminant analysis. The extracted features shows the abnormal type 2 diabetes and for predicting heart disease by classifying the extracted data using VGG-16 Net_gradient NN. Experimental analysis has been carried out in terms of accuracy, precision, recall, F-1 score, RMSE and MAP for various diabetes dataset. Proposed technique attained accuracy of 96%, precision of 67%, recall of 79%, F-1 score of 63%, RMSE of 66% and MAP of 68%.

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References

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Overall proposed architecture

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Published

30.12.2022

How to Cite

Pandey, A. K. ., Shreyanth S., J. Prabhakaran, Bodhankar, A. ., Kumar, A. ., & Sateesh, N. . (2022). Big Data Predictive Analysis for Type-2 Diabetes Based Heart Disease Using Feature Extraction and Classification by Machine Learning Architectures. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 138 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2424

Issue

Vol. 10 No. 3s (2022)

Section

Research Article

License

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