A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis

Open Life Sciences · 6 authors, 6 centres

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TL;DR

This study used data mining algorithms, specifically Support Vector Machine and Multilayer Perceptron Ensemble Neural Network, to classify a dataset of malignant mesothelioma patients.

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The study applied data mining techniques, Support Vector Machine and Multilayer Perceptron Ensemble Neural Network, to a dataset containing both healthy individuals and malignant mesothelioma patients to classify the illness. The research followed a quantitative approach using 10-fold cross-validation over 5 runs to evaluate classifier performance. The implications are that these computational methods can serve as a learning-based decision support system to aid in medical diagnosis.

STUDY TYPE

other

SPECIALTY

oncology

SUMMARISED BY

AI pipeline

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ORIGINAL PAPER

PMC10638843

A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis

Open Life Sciences

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ABSTRACT

Abstract Lung cancer is a substantial health issue globally, and it is one of the main causes of mortality. Malignant mesothelioma (MM) is a common kind of lung cancer. The majority of patients with MM have no symptoms. In the diagnosis of any disease, etiology is crucial. MM risk factor detection procedures include positron emission tomography, magnetic resonance imaging, biopsies, X-rays, and blood tests, which are all necessary but costly and intrusive. Researchers primarily concentrated on the investigation of MM risk variables in the study. Mesothelioma symptoms were detected with the help of data from mesothelioma patients. The dataset, however, included both healthy and mesothelioma patients. Classification algorithms for MM illness diagnosis were carried out using computationally efficient data mining techniques. The support vector machine outperformed the multilayer perceptron ensembles (MLPE) neural network (NN) technique, yielding promising findings. With 99.87% classification accuracy achieved using 10-fold cross-validation over 5 runs, SVM is the best classification when contrasted to the MLPE NN, which achieves 99.56% classification accuracy. In addition, SPSS analysis is carried out for this study to collect pertinent and experimental data.

CITATION EXPORT

Abdulla Mousa Falah Alali, Dhyaram Lakshmi Padmaja, Mukesh Soni et al.. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis. Open Life Sciences. (2023). https://doi.org/10.1515/biol-2022-0746

AUTHORS

6 · 6 CENTRES

Abdulla Mousa Falah Alali, Dhyaram Lakshmi Padmaja, Mukesh Soni, Muhammad Attique Khan, Faheem Khan, Isaac Ofori

Department of Computer Science, Isra University, Amman, Jordan
Department of Information Technology, Anurag University, Hyderabad, Telangana, India
Department of CSE, University Centre for Research & Development, Chandigarh University, Mohali, Punjab 140413, India
Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
Department of Computer Engineering, Gachon University, Seongnam-si, South Korea
Department of Environmental and Safety Engineering, University of Mines and Technology (UMaT), Tarkwa, Ghana