Ovarian Cancer Classification Using Serum Proteomic Profiling and Wavelet Features A Comparison of Machine Learning and Features Selection Algorithms

This article presents a comparison of 4 widely used machine learning (ML) algorithms and 2 feature selection algorithms. The ML algorithms were applied on low-resolution surface-enhanced laser desorption/ionization–time-of-flight data sets for ovarian cancer diagnosis, by extracting wavelet features from spectrometer data and feeding them to the classifiers. The comparison is done by fusion of both selected features using the different algorithms with the classifiers, and then they were compared by measuring their classification test accuracy, sensitivity, and specificity values. Results show that all the presented ML algorithms performed well, with different feature selection algorithms all exceeding 90% accuracy.
Source: Journal of Clinical Engineering - Category: Medical Devices Tags: FEATURE ARTICLES Source Type: research