A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network

Manur, Manohar; Christ,; Pani, Alok Kumar; Kumar, Pankaj

doi:10.22266/ijies2020.0430.04

Cited by 12 publications

(17 citation statements)

References 16 publications

(26 reference statements)

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“…Sequence labeling can be made more efficient by providing both future and previous input context for a certain period. An integrated LSTM in two directions (forward and backward) solves the problem of only having previous inputs for all the hidden states (Manur et al, 2020). Every time a sequence is given, it is checked in the forward direction (left to right), and in the backward direction (right to left) once more (Cai et al,2021).…”

Section: Bidirectional Long Short-term Memory (Bilstm)mentioning

confidence: 99%

Boosting Arabic Named Entity Recognition with K-Fold Cross Validation on LSTM and Bi-LSTM Models

Alsultani¹,

Aliwy²

2022

Journal of Computer Science

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Named-Entity-Recognition (NER) is one of the most important Information-Extraction (IE) use cases, which is used to improve the performance of Natural Languages Processing (NLP) tasks, such as Relation-Extraction (RE), Question-Answering (QA). Recently, Arabic NER is tackled in different ways by researchers. In this study, we assess the performance of two widely used models, namely, LSTM and Bi-LSTM on the NER task in the Arabic language and perform a comparative study between these models. In contrast to the traditional data partition technique widely used during the training, we employ the technique of k-fold cross-validation to improve the performance of each model. The experimental results reveal that the performance of all models is improved when k-fold cross-validation is applied. Additionally, according to our experiment results, the Bi-LSTM model outperforms the LSTM model in terms of our evaluation metric. We achieve the best F1 score of 94.17% with CNN-Bi-LSTM-CRF. An ablation study on k-fold cross-validation demonstrates that the F1 score increased from 87.28 to 94.17%.

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Section: Bidirectional Long Short-term Memory (Bilstm)mentioning

confidence: 99%

Boosting Arabic Named Entity Recognition with K-Fold Cross Validation on LSTM and Bi-LSTM Models

Alsultani¹,

Aliwy²

2022

Journal of Computer Science

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“…Howsoever, this technique failed to consider a large number of attributes for analyzing a level of the disease effectively. Manur et al 14 designed a bi‐directional long short term memory with conditional random field (BiLSTM‐CRF) for heart disease prediction. This method achieved enhanced classification accuracy.…”

Section: Motivationsmentioning

confidence: 99%

“…Moreover, the deep learning method is a robust approach in an artificial intelligence and is utilized in different areas for efficient assessment 12 . However, the deep learning mechanisms efficiently fasten up the tasks in controlling the massive data, which is superior to the various conventional ML techniques, like SVM, Naïve Bayes, and random forest 13,14 . Convolutional neural networks (CNN) was established for recognizing various classes of heart sound in ECG signals, and the personalized deep CNN has been accomplished to categorize ECG data into abnormal as well as normal.…”

Section: Introductionmentioning

confidence: 99%

“…12 However, the deep learning mechanisms efficiently fasten up the tasks in controlling the massive data, which is superior to the various conventional ML techniques, like SVM, Naïve Bayes, and random forest. 13,14 Convolutional neural networks (CNN) was established for recognizing various classes of heart sound in ECG signals, and the personalized deep CNN has been accomplished to categorize ECG data into abnormal as well as normal. Additionally, a recurrent neural network (RNN) also used to forecast the prospective illness based upon the patient representations of electronic health records 15 and also for modeling the temporal relations between the events in electronic health record data.…”

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confidence: 99%

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RideNN‐OptDRN: Heart disease detection using RideNN based feature fusion and optimized deep residual network

Merugula

Padmaja

Veerubommu³

2022

Concurrency and Computation

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Heart disease detection through early-stage syndrome remains as a main confront in present world situation. If it is not detected appropriate time, then this turns out to be the major cause of death. Several existing heart disease detection techniques are developed with lower detection performance and therefore it is very significant to introduce a novel heart disease detection model that poses the potential to detect heart disease from input data. A novel detection approach named, social water cycle algorithm-based deep residual network (SWCA-based DRN) is proposed for classification of heart disease. The developed SWCA algorithm is a newly designed by the hybridization of social optimization algorithm and water cycle algorithm. Here, an input data is initially preprocessed and the feature fusion procedure is carried out RV coefficient enabled rider optimization algorithm-based neural network. With the fused feature result, heart disease classification is performed utilizing a DRN classifier where training procedure of DRN is done by proposed optimization algorithm, named SWCA. Furthermore, developed SWCA-enabled DRN technique outperformed different other present heart disease detection approaches and attained superior performance concerning the performance measures, like testing accuracy, sensitivity, and specificity with highest values of 0.941, 0.954, and 0.925. K E Y W O R D S deep residual network, heart disease, rider optimization algorithm, social optimization algorithm, water cycle algorithm INTRODUCTIONA heart is a fundamental organ which expels blood, with life-giving oxygen and supplements to various body tissues. If pumping action of heart becomes ineffective, essential organs such as kidneys as well as brain suffering, and if the heart functioning ceases completely, demise happens in a minute. Heart ailment has been regarded as one of fatal ailments across the world. Life is entirely reliant itself on an effective functioning of heart.The various indications of heart disease encompass weakness of the physical body, fatigue, swollen feet, and shortness of breath. 1,2 In addition, one of the most human life-threatening diseases around a world is heart ailments, due to variations in lifestyle and lack of physical exercise, and obesity is more general among human lives. According to World Health Organization (WHO) data, human deaths are almost 31% of overall and out of which 85% is due to stroke and heart failure. About 17 million people are losing their life due to heart disease every year. 3 Moreover, heart disease is caused based on the threat factors in lifestyle behavior, like sex, age, cholesterol, family history, obesity or inhaling cigarette smoke, improper diet, blood sugar levels, alcohol, high blood pressure, eating foods that are high in fat, and body weight. Certain threat factors are submissive. Furthermore, heart ailment can be partitioned in seven different kinds, namely, arrhythmia, cardiomyopathy, angina pectoris, coronary heart infection, congenital

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“…e system was studied using StatLog datasets. For the Cleveland dataset, important risk factors, such as age, RestECG, ST Depression (Slope), and so on are removed from the model [16]. For the standardization of the proposed approach, no significance tests are performed, and StatLog dataset [17] and Z-Alizadeh Sani dataset are used.…”

Section: Introductionmentioning

confidence: 99%

A Method for Improving Prediction of Human Heart Disease Using Machine Learning Algorithms

Saboor¹,

Usman²,

Ali

et al. 2022

Mobile Information Systems

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A great diversity comes in the field of medical sciences because of computing capabilities and improvements in techniques, especially in the identification of human heart diseases. Nowadays, it is one of the world’s most dangerous human heart diseases and has very serious effects the human life. Accurate and timely identification of human heart disease can be very helpful in preventing heart failure in its early stages and will improve the patient’s survival. Manual approaches for the identification of heart disease are biased and prone to interexaminer variability. In this regard, machine learning algorithms are efficient and reliable sources to detect and categorize persons suffering from heart disease and those who are healthy. According to the recommended study, we identified and predicted human heart disease using a variety of machine learning algorithms and used the heart disease dataset to evaluate its performance using different metrics for evaluation, such as sensitivity, specificity, F-measure, and classification accuracy. For this purpose, we used nine classifiers of machine learning to the final dataset before and after the hyperparameter tuning of the machine learning classifiers, such as AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB. Furthermore, we check their accuracy on the standard heart disease dataset by performing certain preprocessing, standardization of dataset, and hyperparameter tuning. Additionally, to train and validate the machine learning algorithms, we deployed the standard K-fold cross-validation technique. Finally, the experimental result indicated that the accuracy of the prediction classifiers with hyperparameter tuning improved and achieved notable results with data standardization and the hyperparameter tuning of the machine learning classifiers.

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A Prediction Technique for Heart Disease Based on Long Short Term Memory Recurrent Neural Network

Cited by 12 publications

References 16 publications

Boosting Arabic Named Entity Recognition with K-Fold Cross Validation on LSTM and Bi-LSTM Models

Boosting Arabic Named Entity Recognition with K-Fold Cross Validation on LSTM and Bi-LSTM Models

RideNN‐OptDRN: Heart disease detection using RideNN based feature fusion and optimized deep residual network

A Method for Improving Prediction of Human Heart Disease Using Machine Learning Algorithms

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