scholarly journals Heart disease classification based on performance measures using a deep learning model

2021 ◽  
Author(s):  
İpek BALIKÇI ÇİÇEK ◽  
Zeynep KÜÇÜKAKÇALI
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Performance Measures ◽  
Learning Model ◽  
Disease Classification ◽  
Deep Learning Model

AOHDL: Archimedes Optimization Based Hybrid Deep Learning Model for Soybean Plant Disease Classification

2021 ◽  
Author(s):  
J. Annrose ◽  
N. Herald Anantha Rufus ◽  
C. R. Edwin Selva Rex ◽  
D. Godwin Immanuel
Keyword(s):  
Deep Learning ◽  
Optimization Algorithm ◽  
Short Term Memory ◽  
Learning Model ◽  
Disease Classification ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Soybean Plant ◽  
Lower Accuracy ◽  
Deep Learning Model

Abstract Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

scholarly journals Soybean Plant Disease Classification using Archimedes Optimization Algorithm based Hybrid Deep Learning Model

2021 ◽  
Author(s):  
J. Annrose ◽  
N. Herald Anantha Rufus ◽  
C. R. Edwin Selva Rex ◽  
D. Godwin Immanuel
Keyword(s):  
Deep Learning ◽  
Optimization Algorithm ◽  
Short Term Memory ◽  
Learning Model ◽  
Disease Classification ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Soybean Plant ◽  
Lower Accuracy ◽  
Deep Learning Model

Abstract Bean which is botanically called Phaseolus vulgaris L belongs to the Fabaceae family.During bean disease identification, unnecessary economical losses occur due to the delay of the treatment period, incorrect treatment, and lack of knowledge. The existing deep learning and machine learning techniques met few issues such as high computational complexity, higher cost associated with the training data, more execution time, noise, feature dimensionality, lower accuracy, low speed, etc. To tackle these problems, we have proposed a hybrid deep learning model with an Archimedes optimization algorithm (HDL-AOA) for bean disease classification. In this work, there are five bean classes of which one is a healthy class whereas the remaining four classes indicate different diseases such as Bean halo blight, Pythium diseases, Rhizoctonia root rot, and Anthracnose abnormalities acquired from the Soybean (Large) Data Set.The hybrid deep learning technique is the combination of wavelet packet decomposition (WPD) and long short term memory (LSTM). Initially, the WPD decomposes the input images into four sub-series. For these sub-series, four LSTM networks were developed. During bean disease classification, an Archimedes optimization algorithm (AOA) enhances the classification accuracy for multiple single LSTM networks. MATLAB software implements the HDL-AOA model for bean disease classification. The proposed model accomplishes lower MAPE than other exiting methods. Finally, the proposed HDL-AOA model outperforms excellent classification results using different evaluation measures such as accuracy, specificity, sensitivity, precision, recall, and F-score.

scholarly journals Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

2021 ◽  
Vol 11 (6) ◽  
pp. 5467
Author(s):  
Surenthiran Krishnan ◽  
Pritheega Magalingam ◽  
Roslina Ibrahim
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Learning Model ◽  
Disease Prediction ◽  
The Neural Network ◽  
Proposed Model ◽  
Deep Learning Model

<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.</span>

Plant leaf disease classification using EfficientNet deep learning model

2021 ◽  
Vol 61 ◽  
pp. 101182
Author(s):  
Ümit Atila ◽  
Murat Uçar ◽  
Kemal Akyol ◽  
Emine Uçar
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Disease Classification ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Deep Learning Model

scholarly journals Development of Machine Learning Model for Diagnostic Disease Prediction Based on Laboratory Tests

2021 ◽  
Author(s):  
Dong Jin Park ◽  
Min Woo Park ◽  
Homin Lee ◽  
Young-Jin Kim ◽  
Yeongsic Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Linear Models ◽  
Confusion Matrix ◽  
Learning Model ◽  
Disease Classification ◽  
Machine Learning Model ◽  
Tree Models ◽  
Hidden Layer ◽  
Deep Learning Model

Abstract Artificial intelligence is a concept that includes machine learning and deep learning. The deep learning model used in this study corresponds to DNN (deep neural network) by utilizing two or more hidden layers. In this study, MLP (multi-layer perceptron) and machine learning models (XGBoost, LGBM) were used. An MLP consists of at least three layers: an input layer, a hidden layer, and an output layer. In general, tree models or linear models using machine learning are widely used for classification. We analyzed our data by applying deep learning (MLP) to improve the performance, which showed good results. The deep learning and ML models showed differences in predictive power and disease classification patterns. We used a confusion matrix and analyzed feature importance using the SHAP value method. Here, we present a protocol to confirm that the use of deep learning can show good performance in disease classification using hospital numerical structured data (laboratory test).

scholarly journals Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

2022 ◽  
Vol 70 (2) ◽  
pp. 2097-2111
Author(s):  
R. H. Aswathy ◽  
P. Suresh ◽  
Mohamed Yacin Sikkandar ◽  
S. Abdel-Khalek ◽  
Hesham Alhumyani ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Deep Learning ◽  
Kidney Disease ◽  
Learning Model ◽  
Disease Classification ◽  
Deep Learning Model

scholarly journals Implementation of an incremental deep learning model for survival prediction of cardiovascular patients

2021 ◽  
Vol 10 (1) ◽  
pp. 101
Author(s):  
Sanaa Elyassami ◽  
Achraf Ait Kaddour
Keyword(s):  
Neural Networks ◽  
Heart Disease ◽  
Deep Learning ◽  
Learning Model ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Survival Prediction ◽  
Proposed Model ◽  
The Impact ◽  
Deep Learning Model

<span lang="EN-US">Cardiovascular diseases remain the leading cause of death, taking an estimated 17.9 million lives each year and representing 31% of all global deaths. The patient records including blood reports, cardiac echo reports, and physician’s notes can be used to perform feature analysis and to accurately classify heart disease patients. In this paper, an incremental deep learning model was developed and trained with stochastic gradient descent using feedforward neural networks. The chi-square test and the dropout regularization have been incorporated into the model to improve the generalization capabilities and the performance of the heart disease patients' classification model. The impact of the learning rate and the depth of neural networks on the performance were explored. The hyperbolic tangent, the rectifier linear unit, the Maxout, and the exponential rectifier linear unit were used as activation functions for the hidden and the output layer neurons. To avoid over-optimistic results, the performance of the proposed model was evaluated using balanced accuracy and the overall predictive value in addition to the accuracy, sensitivity, and specificity. The obtained results are promising, and the proposed model can be applied to a larger dataset and used by physicians to accurately classify heart disease patients.</span>

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