Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions


  • Rebecca Nyasuguta Arika School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
  • Agnes Mindila School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
  • W. Cheruiyo School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya



Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body. Previously, researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, K-means, Fuzzy C-means, Neural Network, Principle Component Analysis (PCA) and Naive Bayes. Unfortunately these algorithms fall short in one way or another due to high levels of computational complexities. For instance, support vector machine employs feature elimination scheme for eradicating data ambiguity and detecting tumors at initial stage. However this scheme is expensive in terms of execution time. On its part, k-means algorithm employs Euclidean distance to determine the distance between cluster centers and data points. However this scheme does not guarantee high accuracy when executed in different iterations. Although the K-nearest Neighbor algorithm employs feature reduction, principle component analysis and 10 fold cross validation methods for enhancing classification accuracy, it is not efficient in terms of processing time. On the other hand, fuzzy c-means algorithm employs fuzziness value and termination criteria to determine the execution time on datasets. However, it proves to be extensive in terms of computational time due to several iterations and fuzzy measure calculations involved. Similarly, convolutional neural network employed back propagation and classification method but the scheme proves to be slow due to frequent retraining. In addition, the neural network achieves low accuracy in its predictions. Since all these algorithms seem to be expensive and time consuming, it necessary to integrate quantum computing principles with conventional machine learning algorithms. This is because quantum computing has the potential to accelerate computations by simultaneously carrying out calculation on many inputs. In this paper, a review of the current machine learning algorithms for breast cancer prediction is provided. Based on the observed shortcomings, a quantum machine learning based classifier is recommended. The proposed working mechanisms of this classifier are elaborated towards the end of this paper.


Algorithm, Quantum computing, Machine learning, Breast cancer, Prediction


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How to Cite

Arika, R. N., Mindila, A., & Cheruiyo, W. (2022). Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions. Journal of Oncology Research, 5(1).