Volume 11, Issue 2 (9-2024)
jhbmi 2024, 11(2): 115-130 |
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Arad F, Mousavi S M, Hosseini S, Amizade M, Sheikhi A. Intelligent Diagnosis of Larynx Cancer Using Machine Learning Methods. jhbmi 2024; 11 (2) :115-130
URL: http://jhbmi.ir/article-1-853-en.html
URL: http://jhbmi.ir/article-1-853-en.html
Mahani Math Center, Afzalipour Research Institute, Shahid Bahonar University of Kerman, Kerman, Iran & Ph.D of Computer Engineering, Associate Professor, Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
Abstract: (260 Views)
Introduction: Larynx cancer can be benign or malignant based on various factors. This research aimed to provide a machine learning-based model to improve the diagnosis of individuals with larynx cancer.
Method: In the first step, the voices of the people who visited the medical centers (including the sounds (A), (E), and (O)) were recorded and considered as a data set. In the second step, the data were classified into three classes (benign cancer, malignant cancer, and healthy) by a specialist. In the third step, the data cleaning was done. In the fourth step, the features related to sound were extracted from the data. In the fifth step, five machine learning models including SVM, Decision Tree, Naïve Bayes, MLP, and Random Forest were implemented on the data set. Finally, the performance of the models was evaluated using evaluation criteria such as accuracy, F-score, and other evaluation criteria.
Results: The results of the implementation showed that the SVM model had a higher accuracy than other models for the sound (A) and sound (O) with an accuracy of 0.818, and the sound (E) with an accuracy of 0.818 in the model MLP had the highest accuracy.
Conclusion: The present study evaluated machine learning models for the diagnosis of laryngeal cancer based on audio data. The results showed that the use of the SVM model for the diagnosis of laryngeal cancer can help diagnose this disease more accurately and provide reliable results.
Method: In the first step, the voices of the people who visited the medical centers (including the sounds (A), (E), and (O)) were recorded and considered as a data set. In the second step, the data were classified into three classes (benign cancer, malignant cancer, and healthy) by a specialist. In the third step, the data cleaning was done. In the fourth step, the features related to sound were extracted from the data. In the fifth step, five machine learning models including SVM, Decision Tree, Naïve Bayes, MLP, and Random Forest were implemented on the data set. Finally, the performance of the models was evaluated using evaluation criteria such as accuracy, F-score, and other evaluation criteria.
Results: The results of the implementation showed that the SVM model had a higher accuracy than other models for the sound (A) and sound (O) with an accuracy of 0.818, and the sound (E) with an accuracy of 0.818 in the model MLP had the highest accuracy.
Conclusion: The present study evaluated machine learning models for the diagnosis of laryngeal cancer based on audio data. The results showed that the use of the SVM model for the diagnosis of laryngeal cancer can help diagnose this disease more accurately and provide reliable results.
Type of Study: Original Article |
Subject:
Artificial Intelligence in Healthcare
Received: 2024/03/12 | Accepted: 2024/08/3
Received: 2024/03/12 | Accepted: 2024/08/3
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