Diagnosis and Classification of Cancer Using Machine Learning Techniques

There are many types of cancers that are dangerous for human beings. Especially Breast cancer is one of the most common reasons for increasing death in women worldwide. Diagnosis of breast cancer at the initial stage can minimize the mortality rate significantly. It is possible using machine learning algorithms. A cancer patient can survive longer if the model is more accurate and precise. A smart framework is essential to detect and classify breast cancer at an early stage with high accuracy and precision. In this study, a model is implemented using machine learning techniques for the detection and classification of breast cancer. We have used a support vector classifier (SVC), Naive Bayes, logistic regression, and k-nearest neighbor algorithms for classification purposes. After obtaining results, model performance is analyzed based on different evaluation matrices. Sensitivity, selectivity, accuracy, precision, and
$f1$
-score is taken into consideration as the performance measures. It is detected that the support vector classifier accomplished the maximum accuracy (98%), precision, and
$f1$
-score. Python programming language and Scikit-learn library are used for this work.