Classifying lymphoma and tuberculosis case reports using machine learning algorithms
| Autor: | Moanda Diana Pholo, Yskandar Hamam, Chunling Du, Abdelbaset A. Khalaf |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Control and Optimization
Tuberculosis Computer Networks and Communications Computer science computer.software_genre Machine learning Clinical decision support system Lymphoma Machine learning Medical diagnosis Natural language processing Tuberculosis Computer Science (miscellaneous) medicine Electrical and Electronic Engineering Medical diagnosis Instrumentation Recall Point (typography) business.industry Document clustering Perceptron medicine.disease Hardware and Architecture Control and Systems Engineering Artificial intelligence business computer Algorithm Web scraping Information Systems |
| Zdroj: | Bulletin of Electrical Engineering and Informatics. 10:2857-2865 |
| ISSN: | 2302-9285 2089-3191 |
| DOI: | 10.11591/eei.v10i5.3132 |
| Popis: | Available literature reports several lymphoma cases misdiagnosed as tuberculosis, especially in countries with a heavy TB burden. This frequent misdiagnosis is due to the fact that the two diseases can present with similar symptoms. The present study therefore aims to analyse and explore TB as well as lymphoma case reports using Natural Language Processing tools and evaluate the use of machine learning to differentiate between the two diseases. As a starting point in the study, case reports were collected for each disease using web scraping. Natural language processing tools and text clustering were then used to explore the created dataset. Finally, six machine learning algorithms were trained and tested on the collected data, which contained 765 lymphoma and 546 tuberculosis case reports. Each method was evaluated using various performance metrics. The results indicated that the multi-layer perceptron model achieved the best accuracy (93.1%), recall (91.9%) and precision score (93.7%), thus outperforming other algorithms in terms of correctly classifying the different case reports. |
| Databáze: | OpenAIRE |
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