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Classification of stroke disease using machine learning algorithms

  • Priya Govindarajan
  • Ravichandran Kattur Soundarapandian
  • Amir H. GandomiEmail author
  • Rizwan Patan
  • Premaladha Jayaraman
  • Ramachandran Manikandan
Intelligent Biomedical Data Analysis and Processing
  • 64 Downloads

Abstract

This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients’ symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69.

Keywords

Stroke Tagging Maximum entropy Data pre-processing Classification Machine learning 

Notes

Acknowledgements

We are grateful to Dr. Sundarrajan S, Neurologist, Sugam Multispecialty Hospital, for permitting us to access the real-time data of the patients and for his valuable suggestions in classifying the type of strokes. We also thank the management of Sugam Multispecialty Hospital, Kumbakonam, for their assistance in collecting the case sheets. We acknowledge the Department of Science and Technology, India, for providing financial support through INSPIRE fellowship (No. IF120649) to carry out this research work. The second author also thanks Department of Science & Technology for financial aid from grant No.SR/FST/ETI-349/2013.

Compliance with ethical standards

Conflict of interest

There is no conflict of interest among the authors to publish this article.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceSASTRA Deemed UniversityKumbakonamIndia
  2. 2.Department of Information and Communication TechnologySASTRA Deemed UniversityThanjavurIndia
  3. 3.School of BusinessStevens Institute of TechnologyHobokenUSA
  4. 4.School of Computing Science and EngineeringGalgotias UniversityGreater NoidaIndia

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