Classification of stroke disease using machine learning algorithms

Priya Govindarajan, Ravichandran Kattur Soundarapandian, Amirhossein Gandomi, Rizwan Patan, Premaladha Jayaraman, Ramachandran Manikandan

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalNeural Computing and Applications
DOIs
StateAccepted/In press - Jan 1 2019

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Learning algorithms
Learning systems
Neural networks
Syntactics
Information management
Medicine
Support vector machines
Data mining
Entropy
Semantics

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Govindarajan, P., Soundarapandian, R. K., Gandomi, A., Patan, R., Jayaraman, P., & Manikandan, R. (Accepted/In press). Classification of stroke disease using machine learning algorithms. Neural Computing and Applications. https://doi.org/10.1007/s00521-019-04041-y
Govindarajan, Priya ; Soundarapandian, Ravichandran Kattur ; Gandomi, Amirhossein ; Patan, Rizwan ; Jayaraman, Premaladha ; Manikandan, Ramachandran. / Classification of stroke disease using machine learning algorithms. In: Neural Computing and Applications. 2019.
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Classification of stroke disease using machine learning algorithms. / Govindarajan, Priya; Soundarapandian, Ravichandran Kattur; Gandomi, Amirhossein; Patan, Rizwan; Jayaraman, Premaladha; Manikandan, Ramachandran.

In: Neural Computing and Applications, 01.01.2019.

Research output: Contribution to journalArticle

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