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Classification of stroke disease using machine learning algorithms

  • Intelligent Biomedical Data Analysis and Processing
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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.

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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.

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Authors and Affiliations

  1. Department of Computer Science, SASTRA Deemed University, Kumbakonam, India

    Priya Govindarajan

  2. Department of Information and Communication Technology, SASTRA Deemed University, Thanjavur, India

    Ravichandran Kattur Soundarapandian, Premaladha Jayaraman & Ramachandran Manikandan

  3. School of Business, Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Amir H. Gandomi

  4. School of Computing Science and Engineering, Galgotias University, Greater Noida, India

    Rizwan Patan

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  1. Priya Govindarajan
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  2. Ravichandran Kattur Soundarapandian
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  3. Amir H. Gandomi
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  5. Premaladha Jayaraman
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Correspondence to Amir H. Gandomi.

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Govindarajan, P., Soundarapandian, R.K., Gandomi, A.H. et al. Classification of stroke disease using machine learning algorithms. Neural Comput & Applic 32, 817–828 (2020). https://doi.org/10.1007/s00521-019-04041-y

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