Blood-based FTIR-ATR spectroscopy coupled with extreme gradient boosting for the diagnosis of type 2 diabetes

Guang, Peiwen; Huang, Wendong; Guo, Liangqia; Yang, Xinhao; Huang, Furong; Yang, Mingkang; Wang, Wen; Li, Li

doi:10.1097/md.0000000000019657

Cited by 28 publications

(21 citation statements)

References 27 publications

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“…Random forest (RF) is an ensemble algorithm based on multiple random trees as the base classifiers, and has demonstrated its efficiency for biomedical prediction problems [ 36 ]. XGBoost is a popular fast classifier to handle various biomedical data types, including the spectroscopy spectrum and bioelectrical data [ 37 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

et al. 2021

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Background and Objective: Primary lung cancer is a lethal and rapidly-developing cancer type and is one of the most leading causes of cancer deaths. Materials and Methods: Statistical methods such as Cox regression are usually used to detect the prognosis factors of a disease. This study investigated survival prediction using machine learning algorithms. The clinical data of 28,458 patients with primary lung cancers were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Results: This study indicated that the survival rate of women with primary lung cancer was often higher than that of men (p < 0.001). Seven popular machine learning algorithms were utilized to evaluate one-year, three-year, and five-year survival prediction The two classifiers extreme gradient boosting (XGB) and logistic regression (LR) achieved the best prediction accuracies. The importance variable of the trained XGB models suggested that surgical removal (feature “Surgery”) made the largest contribution to the one-year survival prediction models, while the metastatic status (feature “N” stage) of the regional lymph nodes was the most important contributor to three-year and five-year survival prediction. The female patients’ three-year prognosis model achieved a prediction accuracy of 0.8297 on the independent future samples, while the male model only achieved the accuracy 0.7329. Conclusions: This data suggested that male patients may have more complicated factors in lung cancer than females, and it is necessary to develop gender-specific diagnosis and prognosis models.

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Section: Methodsmentioning

confidence: 99%

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

et al. 2021

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“…In our study we used IR spectroscopy, which has been adopted for the characterization of biological materials, like tissue sections, cytological and histological specimens or biofluids, particularly serum samples. Using this technique to analyze serum samples, Ghimire et al [ 68 ] identified mouse lymphoma and melanoma, while Gajjaee et al [ 28 ] showed spectral changes in blood samples from patients with ovarian cancer. IR spectroscopy was useful for the estimation of serum cortisol levels in athletes [ 69 ], whereas Lechowicz et al [ 29 ] used FT-IR spectroscopy to enable the differentiation between sera from patients with rheumatoid arthritis (RA) and non-RA sera.…”

Section: Discussionmentioning

confidence: 99%

Use of Fourier-Transform Infrared Spectroscopy (FT-IR) for Monitoring Experimental Helicobacter pylori Infection and Related Inflammatory Response in Guinea Pig Model

Gonciarz

Lechowicz

Urbaniak

et al. 2020

IJMS

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Infections due to Gram-negative bacteria Helicobacter pylori may result in humans having gastritis, gastric or duodenal ulcer, and even gastric cancer. Investigation of quantitative changes of soluble biomarkers, correlating with H. pylori infection, is a promising tool for monitoring the course of infection and inflammatory response. The aim of this study was to determine, using an experimental model of H. pylori infection in guinea pigs, the specific characteristics of infrared spectra (IR) of sera from H. pylori infected (40) vs. uninfected (20) guinea pigs. The H. pylori status was confirmed by histological, molecular, and serological examination. The IR spectra were measured using a Fourier-transform (FT)-IR spectrometer Spectrum 400 (PerkinElmer) within the range of wavenumbers 3000–750 cm−1 and converted to first derivative spectra. Ten wavenumbers correlated with H. pylori infection, based on the chi-square test, were selected for a K-nearest neighbors (k-NN) algorithm. The wavenumbers correlating with infection were identified in the W2 and W3 windows associated mainly with proteins and in the W4 window related to nucleic acids and hydrocarbons. The k-NN for detection of H. pylori infection has been developed based on chemometric data. Using this model, animals were classified as infected with H. pylori with 100% specificity and 97% sensitivity. To summarize, the IR spectroscopy and k-NN algorithm are useful for monitoring experimental H. pylori infection and related inflammatory response in guinea pig model and may be considered for application in humans.

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“…The superior XGBoost method indicates a real potential for the accurate detection of prediabetes within individuals. 83 Guang et al 84 also utilized ATR FT-IR spectroscopy in combination with XGBoost to analyze whole blood samples, here with the intention of diagnosing T2DM. Whole blood was collected from 51 T2DM individuals and 55 healthy individuals.…”

Section: General Application Of Chemometric Methods For Indicating Diabetes Within Various Biological Samplesmentioning

confidence: 99%

Vibrational Spectroscopy for Detection of Diabetes: A Review

Ralbovsky

Lednev

2021

Appl Spectrosc

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Type II diabetes mellitus (T2DM) is a metabolic disorder that is characterized by chronically elevated glucose caused by insulin resistance. Although T2DM is manageable through insulin therapy, the disorder itself is a risk factor for much more dangerous diseases including cardiovascular disease, kidney disease, retinopathy, Alzheimerâs disease, and more. T2DM affects 450 million people worldwide and is attributed to causing over 4 million deaths each year. Current methods for detecting diabetes typically involve testing a personâs glycated hemoglobin levels as well as blood sugar levels randomly or after fasting. However, these methods can be problematic due to an individualâs levels differing on a day-to-day basis or being affected by diet or environment, and due to the lack of sensitivity and reliability within the tests themselves. Vibrational spectroscopic methods have been pursued as a novel method for detecting diabetes accurately and early-on in a minimally invasive manner. This review summarizes recent research, since 2015, which has used infrared or Raman spectroscopy for the purpose of developing a fast and accurate method for diagnosing diabetes. Based on critical evaluation of the reviewed work, vibrational spectroscopy has the potential to improve and revolutionize the way diabetes is diagnosed, thereby allowing for faster and more effective treatment of the disorder.

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Blood-based FTIR-ATR spectroscopy coupled with extreme gradient boosting for the diagnosis of type 2 diabetes

Cited by 28 publications

References 27 publications

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

Use of Fourier-Transform Infrared Spectroscopy (FT-IR) for Monitoring Experimental Helicobacter pylori Infection and Related Inflammatory Response in Guinea Pig Model

Vibrational Spectroscopy for Detection of Diabetes: A Review

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