International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 10, Issue 7 (July 2023), Pages: 219-223

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 Original Research Paper

Respiratory disease classification using selected data mining techniques

 Author(s): 

 Abrahem P. Anqui *

 Affiliation(s):

 College of Technology, Cebu Technological University-Naga Campus, City of Naga, Cebu, Philippines

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0009-0000-1231-569X

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.07.024

 Abstract:

Lung cancer, known for its high mortality rate, continues to claim numerous lives worldwide. Early detection has proven to offer significant advantages, substantially improving the prospects for successful treatment, medication, and the healing process. Despite various classification methods used to identify certain illnesses, their accuracy has often been suboptimal. In this paper, we employ Linear Discriminant Analysis (LDA) as a classifier and dimensionality reduction model to enhance the predictive accuracy of lung cancer presence. This study aims to predict the occurrence of lung cancer by utilizing a set of predictor variables, including gender, age, allergy, swallowing difficulty, coughing, fatigue, alcohol consumption, wheezing, shortness of breath, yellowish finger, chronic disease, smoking, chest pain, anxiety, and peer pressure. The goal is to enable early diagnosis, leading to timely and effective interventions. The results of our investigation demonstrate that LDA achieves an impressive accuracy rate of 92.2% in predicting lung cancer presence, surpassing the performance of the C4.5 and Naïve Bayes classifiers. This finding underscores the potential of LDA as a valuable tool for the early detection of lung cancer, ultimately contributing to improved patient outcomes. Through the utilization of LDA, we hope to advance the field of medical diagnostics and enhance the prospects for successful lung cancer management and treatment.

 © 2023 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Lung cancer, Early detection, Linear discriminant analysis, Predictive accuracy, Medical diagnostics

 Article History: Received 25 February 2023, Received in revised form 11 June 2023, Accepted 12 June 2023

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Anqui AP (2023). Respiratory disease classification using selected data mining techniques. International Journal of Advanced and Applied Sciences, 10(7): 219-223

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 Figures

 Fig. 1 Fig. 2 Fig. 3

 Tables

 Table 1 Table 2

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