Predicting the recurrence of breast cancer using machine learning algorithms
- Resource Type
- Authors
- Osama M. Al-Shari; Amal Alzu’bi; Hassan Najadat; Leming Zhou; Wesam Doulat
- Source
- Multimedia Tools and Applications. 80:13787-13800
- Subject
- Computer Networks and Communications
business.industry
Computer science
Medical record
Cancer
Treatment options
020207 software engineering
02 engineering and technology
medicine.disease
University hospital
Machine learning
computer.software_genre
Breast cancer
Hardware and Architecture
Health care
0202 electrical engineering, electronic engineering, information engineering
Media Technology
medicine
Personalized medicine
Artificial intelligence
business
computer
Algorithm
Software
- Language
- ISSN
- 1573-7721
1380-7501
Breast cancer is one of the most common types of cancer among Jordanian women. Recently, healthcare organizations in Jordan have adopted electronic health records, which makes it feasible for researchers to access huge amounts of medical records. The goal of this study is to predict the recurrence of breast cancer using machine learning algorithms. We developed a Natural Language Processing algorithm to extract key features about breast cancer from medical records at King Abdullah University Hospital (KAUH) in Jordan. We integrated these features and built a medical dictionary for breast cancer. We applied multiple machine learning algorithms on the extracted information to predict the recurrence of breast cancer in patients. Our predicted results were approved by specialist physicians from KAUH. The medical dictionary was created and the accuracy of the data had been validated by targeted users (physicians, researchers). This dictionary can be used for personalized medicine. All machine learning algorithms had a nice performance. OneR algorithm has the best balance of sensitivity and specificity. The medical dictionary will help physicians to choose the most appropriate treatment plan in a short time. The machine learning prediction results can help physicians to make the correct clinical decision regarding their treatment options.