A Feature Fusion Based Hybrid Approach for Breast Cancer Classification


  • Fatima Iftikhar Department of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
  • Hafiz Muhammad Mueez Amin Department of Computer Science, MNS-University of Agriculture, Multan, Pakistan
  • Ghulam Abbas Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan




Breast cancer, Machine learning, Breast tumor, Classification


A detected type of cancer is breast cancer commonly in women. According to some estimate one in nine women is diagnosed with breast cancer. It is unfortunate that due to a lack of proper facilities, the diagnosis of breast cancer in patients is being delayed, which is leading to an increase in the possible death rate. Many different statistical methods and Machine Learning algorithms are often employed in the study to make breast cancer detection more accurate. Machine learning (ML) has allowed doctors to achieve remarkable results, and healthcare is using ML-based models to detect breast cancer in women. This allows analyzing the healthcare data and uses the traditional computer-aided detection (CAD) to assess breast cancer. Machine learning has become an accepted clinical practice and allows doctors to evaluate the ML model to detect breasts at an early stage. A major aim is to diagnose patients with breast cancer by analyzing the data of patients and classifying them into two categories, having diagnosis results as Benign "B" or Malignant “M”. In this study different machine learning algorithms are used to classify cancer as either its malignant or benign. The Kaggle data set was used for applying these algorithms to get the best accuracy. MLP is more efficient and accurate algorithm to classify the breast tumor. And here also fitted the matthews_corrcoef for MLP is 0.89% and accuracy score for the random forest is 0.94%.




How to Cite

Fatima Iftikhar, Hafiz Muhammad Mueez Amin, & Ghulam Abbas. (2022). A Feature Fusion Based Hybrid Approach for Breast Cancer Classification. Journal of Computing & Biomedical Informatics, 3(01), 243–256. https://doi.org/10.56979/301/2022/37