Abstract
One of the most widespread diseases among women today is breast cancer. Early and accurate diagnosis is key in rehabilitation and treatment. The usage of mammograms has some uncertainties in the detection rate. To develop tools for physicians for effective and early detection and diagnosis, machine learning techniques can be adopted. The introduction of Machine Learning (ML) in developing the tool will increase the survival rate of patients with breast cancer. This research work proposed different six ML techniques; Logistic Regression, Linear Discriminant Analysis, Decision Tree (DT), KNN, Naïve Bayes (NB), and Support Vector Machine (SVM), and then recommended the model with the highest accuracy for breast cancer detection. The experiment was carried out in a python environment and all the aforementioned techniques were validated with Wisconsin Breast Cancer dataset and evaluated with accuracy, precision, and recall.
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