Non-Invasive In Vivo Type 2 Diabetes Mellitus Diagnosis Using Raman Spectroscopy in Combination with Machine Learning

Duc, Le Anh; Tung, Nguyen Thanh; Oanh, Tran Thi; Tri, Ngo Quang; Linh, Ngo Thuy

doi:10.1007/s11036-023-02184-w

Mobile Netw Appl

2023

DOI: 10.1007/s11036-023-02184-w

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Non-Invasive In Vivo Type 2 Diabetes Mellitus Diagnosis Using Raman Spectroscopy in Combination with Machine Learning

Le Anh Duc,

Nguyen Thanh Tung,

Tran Thi Oanh

et al.

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2024

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Cited by 2 publications

References 30 publications

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Machine Learning Approach for Early Detection of Diabetes Using Raman Spectroscopy

Quang,

Nguyen,

Viet

2024

Mobile Netw Appl

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Machine Learning Approach for Early Detection of Diabetes Using Raman Spectroscopy

Quang,

Nguyen,

Viet

2024

Mobile Netw Appl

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Artificial Intelligence in Diabetes Management: Revolutionizing the Diagnosis of Diabetes Mellitus; a Literature Review

Keshtkar,

Ayareh,

Atighi

et al. 2024

Shiraz E-Med J

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Context: The diagnostic methods for diabetes mellitus (DM), a chronic metabolic disorder characterized by elevated blood sugar levels, are rapidly evolving thanks to artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL). This review explores the applications of AI in risk assessment and diagnosing different types of diabetes. Evidence Acquisition: The review highlights the effectiveness of various ML models, including support vector machines (SVMs), random forests (RFs), and DL techniques like convolutional neural networks (CNNs), in achieving high diagnostic accuracy. Challenges include limited data availability, interpretability of complex models, and the need for standardized performance metrics. Results: Machine learning methods like SVMs and RFs are highly effective at diagnosing different types of diabetes, and DL techniques like CNNs also show great promise. Conclusions: Overall, AI has immense potential to revolutionize diabetes diagnosis by facilitating risk assessment and early detection, improving treatment efficacy, and preventing severe complications.

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Non-Invasive In Vivo Type 2 Diabetes Mellitus Diagnosis Using Raman Spectroscopy in Combination with Machine Learning

Cited by 2 publications

References 30 publications

Machine Learning Approach for Early Detection of Diabetes Using Raman Spectroscopy

Machine Learning Approach for Early Detection of Diabetes Using Raman Spectroscopy

Artificial Intelligence in Diabetes Management: Revolutionizing the Diagnosis of Diabetes Mellitus; a Literature Review

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