Early Detection of Breast Cancer Using Machine Learning Techniques


  • M. Tahmooresi Asia Pacific University of Technology and Innovation (APU), Malaysia.
  • A. Afshar University of Malaya, Malaysia.
  • B. Bashari Rad Asia Pacific University of Technology and Innovation (APU), Malaysia.
  • K. B. Nowshath Asia Pacific University of Technology and Innovation (APU), Malaysia.
  • M. A. Bamiah University of Malaya, Malaysia.


Breast Cancer, Breast Cancer Detection, Medical Images, Machine Learning,


Cancer is the second cause of death in the world. 8.8 million patients died due to cancer in 2015. Breast cancer is the leading cause of death among women. Several types of research have been done on early detection of breast cancer to start treatment and increase the chance of survival. Most of the studies concentrated on mammogram images. However, mammogram images sometimes have a risk of false detection that may endanger the patient’s health. It is vital to find alternative methods which are easier to implement and work with different data sets, cheaper and safer, that can produce a more reliable prediction. This paper proposes a hybrid model combined of several Machine Learning (ML) algorithms including Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Decision Tree (DT) for effective breast cancer detection. This study also discusses the datasets used for breast cancer detection and diagnosis. The proposed model can be used with different data types such as image, blood, etc.


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How to Cite

Tahmooresi, M., Afshar, A., Bashari Rad, B., Nowshath, K. B., & Bamiah, M. A. (2018). Early Detection of Breast Cancer Using Machine Learning Techniques. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(3-2), 21–27. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4706