Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone Sensors

Shichkina, Yulia; Stanevich, Elizaveta; Irishina, Yulia

doi:10.3390/diagnostics10040214

Cited by 5 publications

(4 citation statements)

References 21 publications

(31 reference statements)

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“…PD is one of the main causes of disability in middle-aged and elderly people (Shichkina et al 2020). Early diagnosis and treatment are crucial to control the progression of the disease and improve the prognosis of patients.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Analysis of Diagnostic Value of Electrogastrography for Parkinson’s Disease and its Predictive Value for the Disease Progression

Li¹,

Li²,

Cao³

et al. 2022

Tohoku J. Exp. Med.

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The study aimed to evaluate the diagnostic and prognostic value of indexes detected by electrogastrography (EGG) in Parkinson's disease (PD) patients. 120 early PD patients and 120 healthy controls were recruited, and underwent EGG to detect dominant frequency (DF), instability coefficient of DF (ICDF), low frequency range (LFR), high frequency range (HFR), and normal frequency range (NFR). The receiver operating characteristic (ROC) curve was drawn for the diagnostic value analysis. The motor function was scored with UPDRS. Sniffin' Sticks test was used for the olfactory evaluation, and the TDI score was calculated. The preprandial ICDF of PD patients was significantly higher than that of the control group, and decreased slowly during the late postprandial phase. The levels of LFR%, HFR% and NFR% in PD patients were higher than the control group during both the preprandial and late postprandial phase, and the changes of each index before and after meals were not obvious. Preprandial ICDF value and TDI score had the ability to distinguish PD patients with the AUC of 0.874 and 0.859 respectively. The ICDF detected by EGG has high clinical value in the early diagnosis of PD, and the combination of ICDF and TDI can improve the diagnostic sensitivity and specificity of a single indicator. High ICDF levels during the preprandial phase are related to the poor prognosis of PD patients after treatment.

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Section: Accepted Manuscriptmentioning

confidence: 99%

Analysis of Diagnostic Value of Electrogastrography for Parkinson’s Disease and its Predictive Value for the Disease Progression

Li¹,

Li²,

Cao³

et al. 2022

Tohoku J. Exp. Med.

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show abstract

“…The latest development in machine learning (ML) has improved the computer system's capacity to identify and label images, and project diseases, which leads to making better decisions by utilizing data analysis. Machine learning applications are designed to train a computer system to perform better than a single human (Shichkina et al, 2020). Testing data is used for evaluation, and the supervised learning technique is used to train the model (Maniruzzaman et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Early Diabetes Detection and Diagnosis

Mansouri,

Boulares,

Chabchoub

2024

JOWUA

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In this work, a machine learning (ML)-based e-diagnostic system is suggested specifically for the detection of gestational diabetes mellitus (GDM). Reviewing recent GDM data and outlining the intimate connection between GDM and prediabetic conditions, as well as the potential for future declines in insulin resistance and the emergence of overt Type 2 diabetes, were our goals. The present study explores the application of the K-nearest neighbors (KNN) algorithm to project diabetes diagnosis on the widely-used Pima Indians Diabetes database. The KNN algorithm, a non-parametric, instance-based learning method, was employed to classify individuals as either diabetic or non-diabetic, our objectives were to evaluate the algorithm’s ability to make accurate predictions and explore factors influencing its performance. The study commenced with data preprocessing, including handling missing values, feature scaling, and data splitting into training and testing sets. The KNN classifier was trained and tested using these best-fit parameters. The results of this study revealed a model with an accuracy of approximately 0.76 in predicting diabetes diagnosis. This study looked at the various machine-learning approaches for diabetes patient classification, including recall, accuracy, precision, and F1-score. The study discusses the significance of hyperparameter tuning, data preprocessing, and imbalanced data handling in achieving optimal KNN model performance. Lastly, this study shows how the KNN algorithm may be used to project diabetes using the Pima Indians Diabetes Database. The findings suggest that KNN can serve as a viable tool in the early detection of diabetes, paving the way for more extensive applications in healthcare and predictive modelling.

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“…The Covid-MUNet model is trained using publicly available datasets, including chest X-ray images for multi-class classification (3-class and 4-classes) and CT scans images for binary/multiclass classification (2-classes and 3-classes). In addition, we estimated the COVID-M-UNet model's performance in terms of six metrics, including precision, sensitivity, specificity, F1-score, accuracy, and Matthew's correlation coefficient (MCC) (Shichkina et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Smart Medical Application of Deep Learning (MUNet) for Detection of COVID-19 from Chest Images

Smadi,

Abugabah,

Al-smadi

et al. 2024

JOWUA

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Fighting the outbreak of COVID-19 is now one of humanity's most critical matters. Rapid detection and isolation of infected people are crucial for decelerating the disease's spread. Due to the pandemic, the conventional technique for COVID-19 detection, reverse transcription-polymerase chain reaction, is time-consuming and in small abundance. Therefore, studies have been searching for alternate methods for detecting COVID-19, and thus applying deep learning methods to patients' chest images has been rendering impressive performance. The primary objective of this study is to suggest a technique for COVID-19 detection in chest images that is both efficient and reliable. We propose a deep learning method for COVID-19 classification based on a modified UNet called (Covid-MUNet). The Covid-MUNet model is trained using publicly available datasets, including chest X-ray images for multi-class classification (3-class and 4-classes) and CT scans images for binary/multi-class classification (2-classes and 3-classes). Using chest images, the Covid-MUNet is a successful methodology that helps physicians rapidly identify patients with COVID-19, thereby delaying the fast spread of COVID-19. The proposed model achieved an overall accuracy of 97.44% in classifying three categories (COVID-19, Normal, and Pneumonia) and an accuracy of 96.57% in classifying two categories (COVID-19 and Normal).

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Assessment of the Status of Patients with Parkinson’s Disease Using Neural Networks and Mobile Phone Sensors

Cited by 5 publications

References 21 publications

Analysis of Diagnostic Value of Electrogastrography for Parkinson’s Disease and its Predictive Value for the Disease Progression

Analysis of Diagnostic Value of Electrogastrography for Parkinson’s Disease and its Predictive Value for the Disease Progression

Machine Learning for Early Diabetes Detection and Diagnosis

Smart Medical Application of Deep Learning (MUNet) for Detection of COVID-19 from Chest Images

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