W. Anto Win Shalini, T. Rajalakshmi, S. Vasanthadev Suryakala
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Enhancing the Diagnostic Evaluation of Thyroid Functionality Using Diffuse Reflectance Spectroscopy and Regression Models
Thyroid dysfunction is a prevalent global health concern that necessitates the development of effective and non-invasive screening methods to enable early detection. The application of Diffuse Reflectance Spectroscopy (DRS) in conjunction with preprocessing and predictive models for thyroid dysfunction diagnosis is investigated. The raw spectral data captured from 31 individuals with thyroid dysfunction are subjected to spectral preprocessing techniques like, Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Baseline Correction. The preprocessed data subjected to regression models like Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), LASSO, Random Forest, Ridge Regression, Gaussian Process Regression (GPR), and Bayesian Regression were employed to analyse the efficacy of the models. The PLSR model in concurrence with SNV outperforms other regression models by achieving an R2 of 0.93, RMSE of 0.29, and MSE of 0.08, indicating low predictive error. The goodness of fit was also evaluated using Pearson's chi-squared test.
期刊介绍:
The first international journal dedicated to publishing reviews and original articles from this exciting field, the Journal of Biophotonics covers the broad range of research on interactions between light and biological material. The journal offers a platform where the physicist communicates with the biologist and where the clinical practitioner learns about the latest tools for the diagnosis of diseases. As such, the journal is highly interdisciplinary, publishing cutting edge research in the fields of life sciences, medicine, physics, chemistry, and engineering. The coverage extends from fundamental research to specific developments, while also including the latest applications.