Cost effective and efficient screening of tuberculosis disease with Raman spectroscopy and machine learning algorithms.

The current study presents Raman Spectroscopy (RS) accompanied by machine learning algorithms based on Principle Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) for analysis of tuberculosis (TB). TB positive (diseased), TB negative (cured) and control (healthy) serum samples are considered for inter and intra comparative analysis. Raman spectral differences observed between both TB group and control samples spectra attributed probably to the changes in biomolecules like higher lactate concentration, lowering level of ß-carotene and amide-I band of protein in TB patient's blood samples. Inter comparison between control and TB positive sera samples shows prominent decrease in three extremely intense Raman peaks associated to ß-carotene concentration. Noteworthy spectral differences are also observed among TB positive and TB negative sera samples. The comparison of these Raman results clearly indicate that the blood composition of TB negative patients still showing irregularities in some important elements. Moreover, the Raman spectral differences observed in the data of the control and diseased samples are further highlighted with the help of the machine learning algorithms. In general, a fine correlation has been observed between PCA score plot as well as HCA dendogram with the original Raman findings. Further investigation of such noticeable differences could help in understandings regarding the existing threshold levels. Moreover in future, it can contribute a lot towards the development of new, modified and more effective screening options.

A comparative study of machine learning classifiers for risk prediction of asthma disease.

Asthma is a chronic disease characterized by wheezing, chest tightening and difficulty in breathing due to inflammation of lung airways. Early risk prediction of asthma is crucial for proper and effective management. This study presents the use of machine learning approach for risk prediction of asthma by evaluating Raman spectral variations between asthmatic as well as healthy sera samples. Specifically, Raman spectra from 150 asthma and 52 healthy control blood sera samples were acquired. Spectral analyses illustrated significant spectral variations (p < 0.0001) in the asthmatic samples when compared with healthy sera. The existing spectral differences were further exploited by using artificial neural network (ANN) along with support vector machine (SVM) and random forest (RF) algorithms towards machine-assisted classification of the two groups. Quantitative comparison of the evaluation metrics of the classification algorithms showed superior performance of SVM model. Our results indicate that Raman spectroscopy in tandem with SVM can be used in the diagnosis and machine-assisted classification of asthma patients with promising accuracy.