Fingernail Diagnostics: Advancing type II diabetes detection using machine learning algorithms and laser spectroscopy

Rehan, Imran; Rehan, Kamran; Sultana, Sabiha; Ur Rehman, Mujeeb

doi:10.1016/j.microc.2024.110762

Microchemical Journal

2024

DOI: 10.1016/j.microc.2024.110762

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Fingernail Diagnostics: Advancing type II diabetes detection using machine learning algorithms and laser spectroscopy

Imran Rehan,

Kamran Rehan,

Sabiha Sultana

et al.

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2024

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Cited by 3 publications

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Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

Kumar,

Jeya Prabha

et al. 2024

Biomed. Phys. Eng. Express

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The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Present diagnostic techniques depend on invasive procedures, which highlights the need for advancement of non-invasive alternatives for initial disease detection. Machine learning in integration with the optical sensing technology can effectively analyze the signal patterns associated with diabetes. The objective of this research is to develop and evaluate a non-invasive optical-based method combined with machine learning algorithms for the classification of individuals into normal, prediabetic, and diabetic categories. A novel device was engineered to capture real-time optical vascular signals from participants representing the three glycemic states. The signals were then subjected to quality assessment and preprocessing to ensure data reliability. Subsequently, feature extraction was performed using time-domain analysis and wavelet scattering techniques to derive meaningful characteristics from the optical signals. The extracted features were subsequently employed to train and validate a suite of machine learning algorithms. An ensemble bagged trees classifier with wavelet scattering features and random forest classifier with time-domain features demonstrated superior performance, achieving an overall accuracy of 86.6% and 80.0% in differentiating between normal, prediabetic, and diabetic individuals based on the optical vascular signals. The proposed non-invasive optical-based approach, coupled with advanced machine learning techniques, holds promise as a potential screening tool for diabetes mellitus. The classification accuracy achieved in this study warrants further investigation and validation in larger and more diverse populations.

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Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

Kumar,

Jeya Prabha

et al. 2024

Biomed. Phys. Eng. Express

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Integrating Laser-Induced Breakdown Spectroscopy and Ensemble Learning as Minimally Invasive Optical Screening for Diabetes

Rehan,

Khan,

Ullah

2024

Appl Spectrosc

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Diabetes mellitus is a prevalent chronic disease necessitating timely identification for effective management. This paper introduces a reliable, straightforward, and efficient method for the minimally invasive identification of diabetes mellitus through nanosecond pulsed laser-induced breakdown spectroscopy (LIBS) by integrating a state-of-the-art machine learning approach. LIBS spectra were collected from urine samples of diabetic and healthy individuals. Principal component analysis and an ensemble learning classification model were used to identify significant changes in LIBS peak intensity between the diseased and normal urine samples. The model, integrating six distinct classifiers and cross-validation techniques, exhibited high accuracy (96.5%) in predicting diabetes mellitus. Our findings emphasize the potential of LIBS for diabetes mellitus identification in urine samples. This technique may hold potential for future applications in diagnosing other health conditions.

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Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis

Chellappan,

Rajaguru

2024

Bioengineering

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This article investigates the effectiveness of feature extraction and selection techniques in enhancing the performance of classifier accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data, three feature extraction (FE) methods are used, namely Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson’s Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques, Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Models—GMMs, Expectation Maximization—EM, Logistic Regression—LoR, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF, are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data.

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Fingernail Diagnostics: Advancing type II diabetes detection using machine learning algorithms and laser spectroscopy

Cited by 3 publications

References 20 publications

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis

Integrating Laser-Induced Breakdown Spectroscopy and Ensemble Learning as Minimally Invasive Optical Screening for Diabetes

Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis

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