Pattern Recognition for Human Diseases Classification in Spectral Analysis

Pattern recognition is a multidisciplinary area that received more scientific attraction during this period of rapid technological innovation. Today, many real issues and scenarios require pattern to aid in the faster resolution complicated problems, particularly those cannot be solved using traditional human heuristics. One common problem dealing with multidimensional data, which prominent studies involving spectral data such as ultraviolet-visible (UV/Vis), infrared (IR), Raman spectroscopy data. UV/Vis, IR, are well-known spectroscopic methods used determine atomic or molecular structure sample various fields. Typically, consists two components: exploratory analysis classification method. Exploratory an approach involves detecting anomalies extracting essential variables, revealing data’s underlying structure. On other hand, techniques algorithms group samples into predetermined category. This article discusses fundamental assumptions, benefits, limitations some including Principal Component Analysis (PCA), Kernel PCA, Successive Projection Algorithm (SPA), Genetic (GA), Partial Least Square Regression (PLS-R), Linear Discriminant (LDA), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Square-Discriminant (PLS-DA) Artificial Neural Network (ANN). The use for disease also highlighted. To conclude, have potential overcome each their distinct limits, there option combining all these create ensemble methods.