Hybrid Glowworm-Fish Swarm Optimization with Fuzzy SVM for Enhanced Ovarian Cancer Detection

This research work presents an attempt to develop a new optimization algorithm known as Hybrid Glowworm Swarm Optimization with Fish Swarm Optimization and Fuzzy Support Vector Machine which can be used for improving the accuracy rate for detection of ovarian cancer based on computer aided diagnosis. The hybrid approach extracts the strong features of both Glowworm Swarm Optimization (GSO) and Fish Swarm Optimization (FSO) on the feature selection process while the Fuzzy Support Vector Machine (FSVM) is used for the classification process. GSO and FSO were used together in order to enhance the search ability and the rate of convergence, to cope with the feature space of ovarian cancer datasets that might be complicated. The FSVM carries this kind of classifications further and addresses inherent uncertainties and overlapping features within data. This integrated method tries to offer more frequent and accurate tool for early detection and may help improve the condition of patients with ovarian cancer. The proposed HGSO-FSO-FSVM framework is yet to be implemented but expected to devise major steps towards enhancement of medical diagnostics especially in early diagnose of ovarian cancer which is crucial in increasing survival rate.