Time Series Classification-Based Correlational Neural Network With Bidirectional LSTM for Automated Detection of Kidney Disease

Bhaskar, Navaneeth; Suchetha, M.; Philip, Nada

doi:10.1109/jsen.2020.3028738

Cited by 35 publications

(11 citation statements)

References 24 publications

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“…Feature engineering is performed using a combination of DT and BiLTCN. BiLTCN is the hybrid of Bidirectional LSTM [ 37 ] and TCN deep learning techniques. DT-BiLTCN approach is utilized as a feature engineering technique in this study.…”

Section: Methodsmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

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Maternal health is an important aspect of women’s health during pregnancy, childbirth, and the postpartum period. Specifically, during pregnancy, different health factors like age, blood disorders, heart rate, etc. can lead to pregnancy complications. Detecting such health factors can alleviate the risk of pregnancy-related complications. This study aims to develop an artificial neural network-based system for predicting maternal health risks using health data records. A novel deep neural network architecture, DT-BiLTCN is proposed that uses decision trees, a bidirectional long short-term memory network, and a temporal convolutional network. Experiments involve using a dataset of 1218 samples collected from maternal health care, hospitals, and community clinics using the IoT-based risk monitoring system. Class imbalance is resolved using the synthetic minority oversampling technique. DT-BiLTCN provides a feature set to obtain high accuracy results which in this case are provided by the support vector machine with a 98% accuracy. Maternal health exploratory data analysis reveals that the health conditions which are the strongest indications of health risk during pregnancy are diastolic and systolic blood pressure, heart rate, and age of pregnant women. Using the proposed model, timely prediction of health risks associated with pregnant women can be made thus mitigating the risk of health complications which helps to save lives.

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Section: Methodsmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

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“…The LSTM unit consists of an input gate I t , an output gate O t and a forget gate F t . The three gates’ activations are computed using the subsequent equations [ 42 ]: …”

Section: Materials and Methodologiesmentioning

confidence: 99%

Deep-kidney: an effective deep learning framework for chronic kidney disease prediction

Saif,

Sarhan,

Elshennawy

2023

Health Inf Sci Syst

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Chronic kidney disease (CKD) is one of today’s most serious illnesses. Because this disease usually does not manifest itself until the kidney is severely damaged, early detection saves many people’s lives. Therefore, the contribution of the current paper is proposing three predictive models to predict CKD possible occurrence within 6 or 12 months before disease existence namely; convolutional neural network (CNN), long short-term memory (LSTM) model, and deep ensemble model. The deep ensemble model fuses three base deep learning classifiers (CNN, LSTM, and LSTM-BLSTM) using majority voting technique. To evaluate the performance of the proposed models, several experiments were conducted on two different public datasets. Among the predictive models and the reached results, the deep ensemble model is superior to all the other models, with an accuracy of 0.993 and 0.992 for the 6-month data and 12-month data predictions, respectively.

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“…The correlation approach will ascertain whether there is a correlation between the input signal and the kernel [23]. A kernel trained from the input data may analyse the signal more successfully because this verifies the similarity between the signals.…”

Section: B Deep Learning Modulementioning

confidence: 99%

Automated Detection of Diabetes From Exhaled Human Breath Using Deep Hybrid Architecture

et al. 2023

Self Cite

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In this paper, we have proposed an automated medical system for detecting type 2 diabetes from exhaled breath. Human breath can be used as a diagnostic sample for detecting many diseases as it contains many gases that are dissolved in the blood. Breath-based analysis stands out among the different non-invasive ways of detection as it provides more accurate predictions and offers many advantages. In this work, the concentration of acetone in the exhaled breath is analysed to detect type 2 diabetes. A new sensing module consisting of an array of sensors is implemented for monitoring the acetone concentration to detect the disease. Deep learning algorithms like Convolutional Neural Networks (CNN) are normally used to automatically analyse medical data to make predictions. Even though the CNN performs well, a few modifications to the network layout can further improve the classification accuracy of the learning model. To analyse the sensor signals to generate predictions, a new deep hybrid Correlational Neural Network (CORNN) is designed and implemented in this research. The proposed detection approach and deep learning algorithm offer improved accuracy when compared to other non-invasive techniques.

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Time Series Classification-Based Correlational Neural Network With Bidirectional LSTM for Automated Detection of Kidney Disease

Cited by 35 publications

References 24 publications

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Deep-kidney: an effective deep learning framework for chronic kidney disease prediction

Automated Detection of Diabetes From Exhaled Human Breath Using Deep Hybrid Architecture

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