Open Access

ARTICLE

Effective Diagnosis of Lung Cancer via Various Data-Mining Techniques

Subramanian Kanageswari¹, D. Gladis², Irshad Hussain^3,*, Sultan S. Alshamrani⁴, Abdullah Alshehri⁵

1 Bharathiar University, Coimbatore, 641046, India
2 Bharathi Women’s College, Chennai, 600108, India
3 Faculty of Electrical and Computer Engineering, University of Engineering and Technology, Peshawar, 25000, Peshawar, Pakistan
4 Department of Information Technology, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
5 Department of Information Technology, Al Baha University, P.O. Box 1988, Al Baha, 65431, Saudi Arabia

* Corresponding Author: Irshad Hussain. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 415-428. https://doi.org/10.32604/iasc.2023.032053

Received 05 May 2022; Accepted 29 June 2022; Issue published 29 September 2022

Abstract

One of the leading cancers for both genders worldwide is lung cancer. The occurrence of lung cancer has fully augmented since the early 19th century. In this manuscript, we have discussed various data mining techniques that have been employed for cancer diagnosis. Exposure to air pollution has been related to various adverse health effects. This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer. We have introduced data mining in lung cancer to air pollution, and our approach includes preprocessing, data mining, testing and evaluation, and knowledge discovery. Initially, we will eradicate the noise and irrelevant data, and following that, we will join the multiple informed sources into a common source. From that source, we will designate the information relevant to our investigation to be regained from that assortment. Following that, we will convert the designated data into a suitable mining process. The patterns are abstracted by utilizing a relational suggestion rule mining process. These patterns have revealed information, and this information is categorized with the help of an Auto Associative Neural Network classification method (AANN). The proposed method is compared with the existing method in various factors. In conclusion, the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.

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Keywords

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