Ovarian cancer detection using optimized machine learning models with adaptive differential evolution

Ovarian cancer is one of the leading causes of mortality among women. The most common detection approach is by tracking related biomarkers to see whether patient has ovarian cancer. Despite emphasis on biomarkers, feature subsets may include physiological features. In this current research, we propose an optimal simultaneous feature weighting/parameter optimization for detecting ovarian cancer occurrence. The weights are optimized using adaptive differential evolution (ADE) with cross validation error and least absolute shrinkage and selection operator (LASSO) regularization as the fitness function. Furthermore, to be in line with the Occam's razor principle, we apply LASSO regularization in the form of weighted l1 norm to reduce features being selected. The regularization constant, lambda, is integral to balance the feature weights and the cross validation error. Using ADE method and, in particular LASSO regularization, we obtain the results showing clear reduction and elimination of features from feature subset. Using K-nearest neighbors (KNN) with lambda = 0.01, 97.24% accuracy (mean) is obtained. Meanwhile, when using support vector machines (SVM) with lambda = 0.01, an accuracy of 96.48% (mean) is achieved. From the viewpoint of machine learning, this, at least, suggests that the proposed approach may be a good solution for optimizing feature weights/parameters at the same time. From the perspective of cancer research, it demonstrates that a plethora of features may be valuable in building a model and should not be discarded for having weak discriminative capability. As a result, the combination of feature subset and non linear mapping plays a significant role.