A Non-invasive Approach to Identify Insulin Resistance with Triglycerides and HDL-c Ratio Using Machine learning

Chakradar, Madam; Aggarwal, Alok; Cheng, Xiaochun; Rani, Anuj; Kumar, Manoj; Shankar, Achyut

doi:10.1007/s11063-021-10461-6

Cited by 36 publications

(7 citation statements)

References 46 publications

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“…Machine learning (ML) algorithms are usually used in data classification problems (Aggarwal et al 2021;Raheja et al 2021;Thapliyal et al 2021;Chakradar et al 2021). The most important step of ML is to successfully extract the essential features that guarantee robust classification.…”

Section: Introductionmentioning

confidence: 99%

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

Yousif

Balaha

Haikal

et al. 2022

J Ambient Intell Human Comput

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Parkinson’s disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features; and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches.

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Section: Introductionmentioning

confidence: 99%

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

Yousif

Balaha

Haikal

et al. 2022

J Ambient Intell Human Comput

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“…The collected data can indeed be controlled and processed using Fuzzy and neural networks. Several sensors collect health information from individuals, and these devices may be like a smart clock or any device supplied by the person [20][21]. The recommended health system can be used as a basis for this idea.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Decision-Making Method based Evaluation of Smart Healthcare Management

Quasim

Shaikh

Shuaib

et al. 2023

Preprint

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The smart medical system is becoming a health policy service that employs wearables, online services and mobile devices to connect to the internet continuously and connect patients, technology and healthcare centers, and then cope efficiently and thoughtfully with the demands of medical ecosystems. This article explores some of the issues facing users to speed up using and accepting intelligent medical technology for access to omnipresent healthcare. The article analyses how Fuzzy Decision Making may be integrated to create better health solutions with an intelligent approach. The smart medical care management system enables the patient to use medical services and services in any place and at any time, including emergency preparedness, medication management and monitoring services. An assessment of this method for managing new concepts comparable to direct health services must be examined. Hence, this paper Smart Healthcare Management Evaluation utilizing Fuzzy Decision Making (SHME-FDM) model, has been proposed to evaluate the technological integration performance. The present paper assesses the security of health data privacy in the intelligent medicare system utilizing the Fuzzy Analytical Hierarchy Process- Preference Technique similar to the ideal solution (Fuzzy AHP-TOPSIS). This paper usage the fuzzy neural network for health care prediction. The test analyses the reliability, error rate accuracy of the fuzzy outcomes. The security hazard analysis data demonstrate that the suggested fuzzy model has the highest risk assessment performance in contrast to the current models.

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“…Ophthalmologists have progressively adopted computer-aided diagnosis as its accuracy has increased in recent years. The system assists doctors in making partial diagnoses and saves both doctors and patients time and effort [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion

Yang

et al. 2022

Micromachines

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Fundus diseases can cause irreversible vision loss in both eyes if not diagnosed and treated immediately. Due to the complexity of fundus diseases, the probability of fundus images containing two or more diseases is extremely high, while existing deep learning-based fundus image classification algorithms have low diagnostic accuracy in multi-labeled fundus images. In this paper, a multi-label classification of fundus disease with binocular fundus images is presented, using a neural network algorithm model based on attention mechanisms and feature fusion. The algorithm highlights detailed features in binocular fundus images, and then feeds them into a ResNet50 network with attention mechanisms to extract fundus image lesion features. The model obtains global features of binocular images through feature fusion and uses Softmax to classify multi-label fundus images. The ODIR binocular fundus image dataset was used to evaluate the network classification performance and conduct ablation experiments. The model’s backend is the Tensorflow framework. Through experiments on the test images, this method achieved accuracy, precision, recall, and F1 values of 94.23%, 99.09%, 99.23%, and 99.16%, respectively.

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A Non-invasive Approach to Identify Insulin Resistance with Triglycerides and HDL-c Ratio Using Machine learning

Cited by 36 publications

References 46 publications

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

Fuzzy Decision-Making Method based Evaluation of Smart Healthcare Management

Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion

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