An Efficient Multi-Disease Diagnosing In Clinical decision Making Using Healthcare Machine Learning Techniques

Authors

  • Ramasamy R Research Scholar, Department of Information Technology, Annamalai University, Chidambaram, Tamil Nadu, India
  • G Pattabirani Assistant Professor, Department of Information Technology, Annamalai University, Chidambaram, Tamil Nadu, India
  • S Parasuraman Co-Guide and Professor, Department of ECE, KarpagaVinayaga College of Engineering and Technology, Chengalpattu, Tamil Nadu, India

Keywords:

HMLT, IML,CART, Clustering, Diagnosis, Data mining, Decision Making, Healthcare.

Abstract

Healthcare uses Machine Learning techniques (HMLT)can be for better application of knowledge and identifying successful prescription patterns for diseases. Usage of computer aided diagnosis for expert opinion learning  have definite advantage. Integrated Machine Learning (IML)with forecasting can provide a dependable and a high quality desirable outcome. Prediction of diseases using machine learningtechniques is a motivating task for augmenting diagnostic accuracy. Hence the objective of this research is usage ofHMLT/IML methodology that can take less time and which can be more economical. The methodology can be useful to predict healthcare diseases. Hence to understand the usage of this research work is to identify the methodology to predict Healthcare diseases from patient’s records and suggest a non-invasive machine learning model.

 

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Published

2024-09-22

How to Cite

Ramasamy R, G Pattabirani, & S Parasuraman. (2024). An Efficient Multi-Disease Diagnosing In Clinical decision Making Using Healthcare Machine Learning Techniques. Journal of Computational Analysis and Applications (JoCAAA), 33(05), 918–925. Retrieved from http://eudoxuspress.com/index.php/pub/article/view/661

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