Jhumi Thapa
Anshu Ghimire
- Assistant Professor Department of Computer Science and Engineering, Nepal Engineering College Bhaktapur Nepal
- Assistant Professor Department of Computer Science and Engineering, Nepal Engineering College Bhaktapur Nepal
Abstract
Breast cancer is the most common type of cancer seen in women in the present day, which is also considered a life-threatening disease. If this cancer can be detected in its early stage it can be a lifesaver for many people around the world. Machine Learning techniques have become one of the hotspots for predicting the early diagnosis of breast cancer. This research work experiments with the two most popularly used supervised machine learning algorithms, K-nearest neighbor, and artificial neural network which will detect breast cancer by training its attributes and find out the most effective with respect to confusion matrix, accuracy, and precision. The findings indicate that the artificial neural network machine achieved superior performance, outperforming all other classifiers with an accuracy rate of 98%. All the work is done in Python programming language using Scikit-Learn library and tensor flow.
Keywords: K-nearest neighbors (KNN), artificial neural network (ANN), breast cancer, machine learning, prediction, Fine Needle Aspirate (FNA)
[This article belongs to Journal of Artificial Intelligence Research & Advances(joaira)]
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Journal of Artificial Intelligence Research & Advances
| Volume | 11 |
| Issue | 01 |
| Received | January 14, 2024 |
| Accepted | April 9, 2024 |
| Published | April 24, 2024 |