RETRACTED ARTICLE: MapReduce-based big data framework using modified artificial neural network classifier for diabetic chronic disease prediction

Ramani, R.; Devi, K. Vimala; Soundar, K. Ruba

doi:10.1007/s00500-020-04943-3

Cited by 23 publications

(8 citation statements)

References 28 publications

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“…With the continuous development of computer technology, machine learning has been widely used in medical and public health research. Previous studies confirmed that neural network, random forest, support vector machine and other models were better than traditional statistical models in disease assessment to some extent [ 15 , 16 ]. One study suggests that using epidemiologically and environmentally dependent transmission rates can potentially generate more practical simulation results [ 17 ].…”

Section: Introductionmentioning

confidence: 92%

A Study on a Neural Network Risk Simulation Model Construction for Avian Influenza A (H7N9) Outbreaks in Humans in China during 2013–2017

Dong

Zhang²,

et al. 2022

IJERPH

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The main purposes of this study were to explore the spatial distribution characteristics of H7N9 human infections during 2013–2017, and to construct a neural network risk simulation model of H7N9 outbreaks in China and evaluate their effects. First, ArcGIS 10.6 was used for spatial autocorrelation analysis, and cluster patterns ofH7N9 outbreaks were analyzed in China during 2013–2017 to detect outbreaks’ hotspots. During the study period, the incidence of H7N9 outbreaks in China was high in the eastern and southeastern coastal areas of China, with a tendency to spread to the central region. Moran’s I values of global spatial autocorrelation of H7N9 outbreaks in China from 2013 to 2017 were 0.080128, 0.073792, 0.138015, 0.139221 and 0.050739, respectively (p < 0.05) indicating a statistically significant positive correlation of the epidemic. Then, SPSS 20.0 was used to analyze the correlation between H7N9 outbreaks in China and population, livestock production, the distance between the case and rivers, poultry farming, poultry market, vegetation index, etc. Statistically significant influencing factors screened out by correlation analysis were population of the city, average vegetation of the city, and the distance between the case and rivers (p < 0.05), which were included in the neural network risk simulation model of H7N9 outbreaks in China. The simulation accuracy of the neural network risk simulation model of H7N9 outbreaks in China from 2013 to 2017 were 85.71%, 91.25%, 91.54%, 90.49% and 92.74%, and the AUC were 0.903, 0.976, 0.967, 0.963 and 0.970, respectively, showing a good simulation effect of H7N9 epidemics in China. The innovation of this study lies in the epidemiological study of H7N9 outbreaks by using a variety of technical means, and the construction of a neural network risk simulation model of H7N9 outbreaks in China. This study could provide valuable references for the prevention and control of H7N9 outbreaks in China.

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Section: Introductionmentioning

confidence: 92%

A Study on a Neural Network Risk Simulation Model Construction for Avian Influenza A (H7N9) Outbreaks in Humans in China during 2013–2017

Dong

Zhang²,

et al. 2022

IJERPH

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“…The OWDANN [7] attained higher precision, specificity, sensitivity and accuracy; however, extending this work to high level is still a challenging issue in order to achieve more precise outcome. The ABP-SCGNN [10] technique exhibited maximal prediction outcomes; however, concerning the requirement of heavy computational power is complicated.…”

Section: 1problem Statementmentioning

confidence: 99%

“…Persistent technological developments offer enormous prospects for physiological innovation as well as expenses reductions, but they additionally constitute a barrier to the incorporation of developing innovation into healthcare practices [7] [8] [9]. Also, a significant amount of work is mainly concentrated on smart healthcare to solve traditional healthcare restricts and meet growing premium healthcare aspirations.…”

Section: Introductionmentioning

confidence: 99%

Healthcare monitoring and Recommendation model: Hybrid Deep learning Enabled Disease Diagnosis Framework

Mallapur

2024

Preprint

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The continuous observation of patients’ health condition as well as monitoring varying crucial symptoms is termed as Healthcare monitoring, which also detect and trace any probable problems. This kind of observation is more crucial for detecting health conditions at early stage especially, recognizing cardiovascular disease in diabetes patient. However, diagnosing cardiovascular disease in diabetes patient is extremely difficult because of the small quantity of labeled data with the prevalence of outliers in the diabetes datasets. This paper proposes a novel healthcare monitoring and recommendation model with hybrid DL based disease diagnosis structure considering the heart disease. The improved z-score normalization is employed to preprocess the input data by scaling the numeric features within a general range. The BWSV-FE based feature extraction process is proposed to extract the entropy features; together with this feature, a raw feature and mutual information features are collected. Subsequently, the hybrid disease prediction model is proposed that includes improved Bi-GRU and LSTM models. Improved Bi-GRU is adopted for efficiently predicting the disease, in which the CCE is updated with the modified MSE loss function by inducing penalty function into this. Moreover, the penalty is computed by employing Gini Index function. Thereby, the hybrid disease prediction model efficiently predicts the diseases’ severity level that aids the doctor for guiding the management of the patients’ health.

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“…The performance metrics are described below for the proposed system to measure the efficiency of the given research [12].…”

Section: Performance Metricsmentioning

confidence: 99%

Prediction of Chronic and Non- Chronic Kidney disease using Modified DBN with Map and Reduce Framework

Ravikumaran¹,

Devi²,

Valarmathi³

2022

Artificial Intelligence and Fuzzy Logic System

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Modern medical information comes in the form of an enormous volume of data that is challenging to maintain using conventional methods. The advancement of big data in the medical and basic healthcare societies is facilitated by precision medical data research, which focuses on comprehending early illness, patient healthcare facilities, and providers. It concentrates primarily on anticipating and discovering direct analysis of some of the substantial health effects that have increased in numerous countries. The existing health industry cannot retrieve detailed information from the chronic disease directory. The advancement of CKD (chronic kidney disease) and the methods used to identify the disease is a difficult task that can lower the cost of diagnosis. In this research, a modified MapReduce and pruning layer-based classification model using the deep belief network (DBN) and the dataset used as CKD were acquired from the UCI repository of machine learning. We have utilized the full potentiality of the DBNs by deploying deep learning methodology to establish better classification of the patient's kidney. Finally, data will be trained and classified using the classification layer and the quality will be compared to the existing method.

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RETRACTED ARTICLE: MapReduce-based big data framework using modified artificial neural network classifier for diabetic chronic disease prediction

Cited by 23 publications

References 28 publications

A Study on a Neural Network Risk Simulation Model Construction for Avian Influenza A (H7N9) Outbreaks in Humans in China during 2013–2017

A Study on a Neural Network Risk Simulation Model Construction for Avian Influenza A (H7N9) Outbreaks in Humans in China during 2013–2017

Healthcare monitoring and Recommendation model: Hybrid Deep learning Enabled Disease Diagnosis Framework

Prediction of Chronic and Non- Chronic Kidney disease using Modified DBN with Map and Reduce Framework

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