Collation of performance parameters on various machine learning algorithms for breast cancer discernment

In clinical practices, machine learning (ML) technology plays an important and rapid growing role as it is likely to help healthcare professionals making decisions and proposing new diagnoses. This research study aims in validating and comparing the performance of various ML models that can help in predicting breast cancer in women. Performance parameters on various ML algorithms for breast cancer dataset has been tested. The testing is performed on 116 participants from dataset. The features of dataset including insulin, glucose, resisting, adiponectin, homeostasis model assessment (HOMA), leptin, age, and index of obesity (MCP1). Many clinical features were measured like BMI. This dataset experimented with 11 classification algorithms such as logistic regression (LR), k-nearest neighbour (kNN), support vector machine (SVM), decision tree (DT), random forest (RF), naïve Bayes and optimum ML algorithms, etc. The research work detected breast cancer from the published Coimbra breast cancer dataset (CBCD). Each classifier has been utilised for various kinds of parameters tuning and for prediction. These results suggested they could be taken as a very meaningful and useful pair of factors to forecast cancer.