A Machine Learning Approach to Predict Diabetes Using Short Recorded Photoplethysmography and Physiological Characteristics

Hettiarachchi, Chirath; Chitraranjan, Charith

doi:10.1007/978-3-030-21642-9_41

Cited by 28 publications

(16 citation statements)

References 8 publications

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“…Other studies [28][29][30] combined software and hardware, as did our study and were related to diabetes; however, they focused only on diabetic retinopathy, which is a complication of diabetes. Similar studies have attempted to use a convenient and noninvasive method to diagnose diabetes and have achieved an accuracy score higher than 0.85 [31,32]. Nevertheless, study [31] Table 2: Example of detected tongue conditions.…”

Section: Resultsmentioning

confidence: 99%

“…Evaluation by TCM Front side of the tongue: light red, the edge of the tongue is sharp red Fire in the liver and gallbladder Fur on the tongue: putrid Indigestion Back side of the tongue: normal QiXue runs smoothly [32] focused on type 2 diabetes. Our research mainly focuses on the diagnosis of diabetes, both type 1 and type 2, in a fast and noninvasive way, using equipment that can be transported conveniently.…”

Section: Detected Tongue Condition Featurementioning

confidence: 99%

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Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

Xiang

Shujin

Hong-fang³

et al. 2021

BioMed Research International

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In this study, we propose a technique for diagnosing both type 1 and type 2 diabetes in a quick, noninvasive way by using equipment that is easy to transport. Diabetes mellitus is a chronic disease that affects public health globally. Although diabetes mellitus can be accurately diagnosed using conventional methods, these methods require the collection of data in a clinical setting and are unlikely to be feasible in areas with few medical resources. This technique combines an analysis of fundus photography of the physical and physiological features of the patient, namely, the tongue and the pulse, which are used in Traditional Chinese Medicine. A random forest algorithm was used to analyze the data, and the accuracy, precision, recall, and F1 scores for the correct classification of diabetes were 0.85, 0.89, 0.67, and 0.76, respectively. The proposed technique for diabetes diagnosis offers a new approach to the diagnosis of diabetes, in that it may be convenient in regions that lack medical resources, where the early detection of diabetes is difficult to achieve.

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Section: Resultsmentioning

confidence: 99%

Section: Detected Tongue Condition Featurementioning

confidence: 99%

Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

Xiang

Shujin

Hong-fang³

et al. 2021

BioMed Research International

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show abstract

“…Type 2 Diabetes detection: Type 2 Diabetes is a common impairment for people that could be extracted from PPG signals using ML approaches. This study was done by Hettiarachchi, C. et al [65] based on PPG signals of smart devices and other additional information such as gender, weight, age, and height. They tested several classification models and found that A 79% area under the ROC curve was achieved by the Linear Discriminant Analysis (LDA).…”

Section: Photoplethysmography (Ppg)mentioning

confidence: 99%

Perspective Chapter: Health Risk Measurement and Assessment Technology – Current State and Future Prospect

Moradi¹,

Simyar²,

Safari³

2024

Biomedical Engineering

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With accelerated technologies, different kinds of health technology devices have been provided to customers that continuously record bio and vital signals. Some of these products are wearable that can be used all day long and during sleeping time. Due to the wearability feature and continuous recording, a vast amount of data can be achieved and analyzed. The recorded data are usually shared with a cloud to implement comprehensive analysis methods where deep and machine learning algorithms play the main role. Finally, they can assess some health factors of the customer and most likely predict future health risks. This chapter shall review the role of the clinical scanners and their valuable data in risk detection, more portable modalities, home-used commercial devices, and emerging techniques which are so potent for future home-used health risks analysis. In the end, we conclude the state-of-the-art and provide our vision about the future of health risk analysis.

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“…Since both PPG and ECG are measured by non-invasive methods, they have recently been used in blood glucose estimation and diabetes detection through machine learning approaches. One of the first studies in this area was by [12], who used the inverse Fourier transform (IFT) to extract features to feed into several machine learning models; [7] identified features related to diabetes from PPG and established the feasibility of prediction with its linear discriminant analysis (LDA); [14] developed logistic regression (LR) modeling to use PPG to perform the classification of diabetes. However, to obtain reliable results, these methods required an abundant amount of attention on dataset processing for the feature extraction.…”

Section: Related Work 21 Photoplethysmography (Ppg) and Electrocardio...mentioning

confidence: 99%

Performer: A Novel PPG-to-ECG Reconstruction Transformer for a Digital Biomarker of Cardiovascular Disease Detection

Lan¹

2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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Diabetes mellitus is a worldwide concern, and early detection can help to prevent serious complications. Low-cost, non-invasive detection methods, which take cardiovascular signals into deep learning models, have emerged. However, limited accuracy constrains their clinical usage. In this paper, we present a new Transformer-based architecture, Higher Dimensional Transformer (HDformer), which takes long-range photoplethysmography (PPG) signals to detect diabetes. The long-range PPG contains broader and deeper signal contextual information compared to the lessthan-one-minute PPG signals commonly utilized in existing research. To increase the capability and efficiency of processing the long range data, we propose a new attention module Time Square Attention (TSA), reducing the volume of the tokens by more than 10x, while retaining the local/global dependencies. It converts the 1-dimensional inputs into 2-dimensional representations and groups adjacent points into a single 2D token, using the 2D Transformer models as the backbone of the encoder. It generates the dynamic patch sizes into a gated mixture-of-experts (MoE) network as decoder, which optimizes the learning on different attention areas. Extensive experimentations show that HDformer results in the state-of-the-art performance (sensitivity 98.4, accuracy 97.3, specificity 92.8, and AUC 0.929) on the standard MIMIC-III dataset, surpassing existing studies. This work is the first time to take long-range, non-invasive PPG signals via Transformer for diabetes detection, achieving a more scalable and convenient solution compared to traditional invasive approaches. The proposed HDformer can also be scaled to analyze general long-range biomedical waveforms. A wearable prototype finger-ring is designed as a proof of concept.

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A Machine Learning Approach to Predict Diabetes Using Short Recorded Photoplethysmography and Physiological Characteristics

Cited by 28 publications

References 8 publications

Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

Perspective Chapter: Health Risk Measurement and Assessment Technology – Current State and Future Prospect

Performer: A Novel PPG-to-ECG Reconstruction Transformer for a Digital Biomarker of Cardiovascular Disease Detection

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