Classification of stroke disease using machine learning algorithms

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 mini...

Full description

Saved in:
Bibliographic Details
Published in:Neural computing & applications Vol. 32; no. 3; pp. 817 - 828
Main Authors: Govindarajan Priya, Soundarapandian Ravichandran Kattur, Gandomi, Amir H, Patan Rizwan, Jayaraman Premaladha, Ramachandran, Manikandan
Format: Journal Article
Language:English
Published: Heidelberg Springer Nature B.V 01.02.2020
Subjects:
Algorithms
Artificial neural networks
Classification
Data acquisition
Data collection
Data management
Data mining
Machine learning
Maximum entropy method
Neural networks
Sheets
Signs and symptoms
Support vector machines
ISSN:0941-0643, 1433-3058
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04041-y