SVM for FT‐MIR prostate cancer classification: An alternative to the traditional methods

In this paper, principal component analysis (PCA), successive projections algorithm (SPA), and genetic algorithm (GA) followed by support vector machines (SVM), combined with Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy were presented as complementary or alternatives tools to the traditional methods for prostate cancer screening and classification. These approaches were applied to analyze tissue samples, and their performances were compared within dependent SVM models and with traditional methods of diagnosis, according to class separation interpretability, time consumption, and figures of merit. The results showed that variable reduction and selection methods followed by SVM can reduce drawbacks of independent SVM analysis. The potential biomarkers indicated by PCA‐SVM, SPA‐SVM, and GA‐SVM were amide I, II, and III; as well as protein regions (1400‐1585 cm−1), followed by DNA/RNA (O—P—O symmetric stretch) (1080 cm−1) and DNA (O—P—O asymmetric stretch) (1230 cm−1) regions. GA‐SVM was the best classification approach, with higher sensitivity (100%) and specificity (80%), particularly in early stages, being better than traditional methods of diagnosis. PCA‐SVM, SPA‐SVM, and GA‐SVM algorithms combined with FT‐MIR spectroscopy were used for detection of prostate cancer based on the analysis of tissue samples. GA‐SVM was the best classification model with 100% sensitivity and 80% specificity, showing the potential of this methodology for prostate cancer screening in comparison with traditional diagnosis techniques.