Novel Approach to Evaluate Classification Algorithms and Feature Selection Filter Algorithms Using Medical Data

In today’s world, hepatitis is a widespread problem related to the medical field, which directly affects the lives of mankind. For patient survival, data mining is essential in predicting future trends using various techniques. This paper uses three feature selection filter algorithms (FSFAs): relie...

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Published in:Journal of Computational and Cognitive Engineering Vol. 2; no. 1; pp. 57 - 67
Main Authors: Masood, Fawad, Masood, Junaid, Zahir, Hina, Driss, Kaouthar, Mehmood, Nasir, Farooq, Hassan
Format: Journal Article
Language:English
Published: 17.05.2022
ISSN:2810-9570, 2810-9503
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Abstract In today’s world, hepatitis is a widespread problem related to the medical field, which directly affects the lives of mankind. For patient survival, data mining is essential in predicting future trends using various techniques. This paper uses three feature selection filter algorithms (FSFAs): relief filter, step disc filter, and Fisher filter algorithm and 15 classifiers using a free data mining Tanagra software having UCI Machine Learning Repository. This process is done on a medical dataset with 20 attributes and 155 instances. As a result, the error rate is obtained in terms of accuracy, which shows the performance of algorithms regarding patient survival. This work also shows the independent comparison of FSFAs with classification algorithms using continuous values and the FSFA without using classification algorithms. This paper shows that the obtained result of the classification algorithm gives promising results in terms of error rate and accuracy.   Received: 6 April 2022 | Revised: 12 May 2022 | Accepted: 16 May 2022   Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
AbstractList In today’s world, hepatitis is a widespread problem related to the medical field, which directly affects the lives of mankind. For patient survival, data mining is essential in predicting future trends using various techniques. This paper uses three feature selection filter algorithms (FSFAs): relief filter, step disc filter, and Fisher filter algorithm and 15 classifiers using a free data mining Tanagra software having UCI Machine Learning Repository. This process is done on a medical dataset with 20 attributes and 155 instances. As a result, the error rate is obtained in terms of accuracy, which shows the performance of algorithms regarding patient survival. This work also shows the independent comparison of FSFAs with classification algorithms using continuous values and the FSFA without using classification algorithms. This paper shows that the obtained result of the classification algorithm gives promising results in terms of error rate and accuracy.   Received: 6 April 2022 | Revised: 12 May 2022 | Accepted: 16 May 2022   Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
Author Masood, Junaid
Masood, Fawad
Mehmood, Nasir
Farooq, Hassan
Zahir, Hina
Driss, Kaouthar
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