Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity

Siddiqui, Mohammad Khubeb; Ahmad, Sultan

doi:10.1590/1678-4324-2020190736

Cited by 17 publications

(10 citation statements)

References 51 publications

(55 reference statements)

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“…In medical diagnosis higher area under curve (AUCs) are required. As a result, in clinical data analysis research, its computation allows the data scientist to simplify the data analysis with predictive knowledge on medical research [38] .…”

Section: Receiver Operating Characteristics (Roc)mentioning

confidence: 99%

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Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

Panwar

Gupta

Siddiqui

et al. 2020

Chaos, Solitons & Fractals

Self Cite

529

368

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Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.

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Section: Receiver Operating Characteristics (Roc)mentioning

confidence: 99%

“…The plotted AUC of ROC as shown in Fig. 6 of proposed model is above the threshold level is calculated as 0.88095 which is a good indication of model and comes under the good rank of classification, and also considered to be 'excellent' in the field of medical diagnosis [38,40] .…”

Section: Receiver Operating Characteristics (Roc)mentioning

confidence: 99%

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

Panwar

Gupta

Siddiqui

et al. 2020

Chaos, Solitons & Fractals

Self Cite

529

368

View full text Add to dashboard Cite

show abstract

“…These metrics help in deciding the best model from the list of models used for the experiment. After the completion of model training, the model's classification performance is measured using the Area Under the Curve (AUC) and Receiver Operating Characteristics (ROC) metrics (Siddiqui et al 2020). These metrics measure the performance of the classification model at different threshold settings.…”

Section: Resultsmentioning

confidence: 99%

A comprehensive comparative analysis of deep learning tools for modeling failures in smart water taps

Muniandy

et al. 2021

Water Supply

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Predicting early-stage failure in smart water taps (SWT) and selecting the most efficient tools to build failure prediction models are many challenges that water institutions face. In this study, three Deep Learning (DL) algorithms, i.e., the Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM), were selected to analyse and determine the most appropriate among them for failure prediction in SWTs. This study uses a historical dataset acquired from smart water withdrawal taps to determine the most efficient DL neural network architecture for failure prediction in the SWT, leading to a hybrid model's development. After a comprehensive evaluation of the three ML models, findings show that a hybrid combination of the CNN and Bi-LSTM (CNN-BiLSTM) models is a better solution for investigating failures in the SWT.

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“…The techniques of data preprocessing assist in obtaining a clean and smooth dataset which enhances the consistency of collected patterns [18,19]. This proposed model uses a data preprocessing system to apply the data preprocessing techniques in the PID dataset.…”

Section: Data Preprocessing Systemmentioning

confidence: 99%

Regression Imputation and Optimized Gaussian Naïve Bayes Algorithm for an Enhanced Diabetes Mellitus Prediction Model

Fathima

Samuel²,

Raja

2021

Braz. arch. biol. technol.

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Diabetes mellitus (DM) is a category of metabolic disorders caused by high blood sugar. The DM affects human metabolism, and this disease causes many complications like Heart disease, Neuropathy, Diabetic retinopathy, kidney problems, skin disorder and slow healing. It is therefore essential to predict the presence of DM using an automated diabetes diagnosis system, which can be implemented using machine learning algorithms. A variety of automated diabetes prediction systems have been proposed in previous studies. Even so, the low prediction accuracy of DM prediction systems is a major issue. This proposed work developed a diabetes mellitus prediction system to improve the diabetes mellitus prediction accuracy using Optimized Gaussian Naive Bayes algorithm. This proposed model using the Pima Indians diabetes dataset as an input to build the DM predictive model. The missing values of an input dataset are imputed using regression imputation method. The sequential backward feature elimination method is used in this proposed model for selecting the relevant risk factors of diabetes disease. The proposed machine learning classifier named Optimized Gaussian Naïve Bayes (OGNB) is applied to the selected risk factors to create an enhanced Diabetes diagnostic system which predicts Diabetes in an individual. The performance analysis of this prediction architecture shows that, over other traditional machine learning classifiers, the Optimized Gaussian Naïve Bayes achieves an 81.85% classifier accuracy. This proposed DM prediction system is effective as compared to other diabetes prediction systems found in the literature. According to our HIGHLIGHTS The aim of this study is to build a diabetes mellitus disease prediction model using machine learning algorithms. To build a diabetes mellitus model for diabetes early prediction, the optimized gaussian naive bayes algorithm is proposed and used as a classifier. 2 Mohammed Mohideen, D.F.; et al.

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Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity

Cited by 17 publications

References 51 publications

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet

A comprehensive comparative analysis of deep learning tools for modeling failures in smart water taps

Regression Imputation and Optimized Gaussian Naïve Bayes Algorithm for an Enhanced Diabetes Mellitus Prediction Model

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