An enhanced Predictive heterogeneous ensemble model for breast cancer prediction

Breast Cancer is one of the most prevalent tumors after lung cancer and is common in both women and men. This disease is mostly asymptomatic in the early stages thus detection is difficult, and it becomes complicated and expensive to be treated in later stages resulting in increased fatality rates. There are comparatively very few pieces of literature that investigated breast cancer employing an ensemble learning for cancer prediction as compared to single classifier approaches. This paper presents a heterogeneous ensemble machine learning approach, to detect breast cancer in the early stages. The proposed approach follows the CRISP-DM process and uses Stacking for building the ensemble model using three different algorithms - K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The performance of this meta classifier is compared with the individual performances of its base classifiers (KNN, SVM, DT) and other single classifiers - Logistic Regression (LR), Artificial Neural Network (ANN), Naive Bayes (NB), Stochastic Gradient Descent (SGD) and a homogenous ensemble model of Random Forest (RF). The top 5 features - Glucose, Resistin, HOMA, Insulin, and BMI are derived by using Chi-Square. Evaluation of the model helps in estimating its consideration for early breast cancer prediction just by using the anthropometric data of humans. Performances of models are compared using metrics such as accuracy, AUC, ROC Curve, f1-score, precision, recall, log loss, and specificity using K-fold cross-validation of 2, 3, 5, 10, and 20 folds. The proposed ensemble model achieved the greatest accuracy of 78 % with the lowest log-loss of 0.56, at K = 20, thus rejecting the Null hypothesis. The derived p-value is 0.014, from the one-tailed t-test, which provides lower significance at proportional to = 0.05.