Distribution preserving train-test split directed ensemble classifier for heart disease prediction

Mohapatra, Debasis; Bhoi, Sourav Kumar; Mallick, Chittaranjan; Jena, Kalyan Kumar; Mishra, Satrujit

doi:10.1007/s41870-022-00868-2

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…Studies have suggested DL improvements, ML support, and increased accuracy. In addition, the results have shown that the application of Artificial Intelligence in the medical field has dramatically supported and significantly improved the accuracy of disease diagnosis, such as studies in [ 10 – 12 ].…”

Section: Related Workmentioning

confidence: 99%

Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam

Nguyen

Pham

et al. 2023

Int. j. inf. tecnol.

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Overcrowding in hospitals in Vietnam has caused many disadvantages in receiving and treating patients. Especially at the stage of receiving and diagnosing procedures taking patients to the treatment departments in the hospital takes up much time. This study proposes a text-based disease diagnosis using text processing techniques (such as Bag of Words, Term Frequency- Inverse Document Frequency, and Tokenizer) combined with classifiers (such as Random Forests (RF), Multi-Layer Perceptron (MLP), Embeddings and Bidirectional Long Short-term memory (LSTM)) on symptoms. As observed from the results, deep Bidirectional LSTM can reach 0.982 in AUC in the classification of 10 diseases on 230,457 samples of pre-diagnosis collected from Vietnam hospitals used in the training and testing phases. The proposed approach is expected to provide a way to automate patient flow in hospitals to improve healthcare in the future.

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Section: Related Workmentioning

confidence: 99%

Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam

Nguyen

Pham

et al. 2023

Int. j. inf. tecnol.

View full text Add to dashboard Cite

show abstract

“…In the past, a lot of effort has been made to incorporate calculations such as machine-based methods to better address this problem. Several research articles describe the use of machine learning in early diagnosis [ 3 ]. Continuous maturity of machine learning techniques and the rapid development of deep learning algorithms in recent years have made it feasible to early detection, and classify various plant ailments with higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

A novel GCL hybrid classification model for paddy diseases

Lamba

Baliyan

Kukreja

2022

Int. j. inf. tecnol.

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The demand for agricultural products increased exponentially as the global population grew. The rapid development of computer vision-based artificial intelligence and deep learning-related technologies has impacted a wide range of industries, including disease detection and classification. This paper introduces a novel neural network-based hybrid model (GCL). GCL is a dataset-augmentation fusion of long-short term memory (LSTM) and convolutional neural network (CNN) with generative adversarial network (GAN). GAN is used for the augmentation of the dataset, CNN extracts the features and LSTM classifies the various paddy diseases. The GCL model is being investigated to improve the classification model’s accuracy and reliability. The dataset was compiled using secondary resources such as Mendeley, Kaggle, UCI, and GitHub, having images of bacterial blight, leaf smut, and rice blast. The experimental setup for proving the efficacy of the GCL model demonstrates that the GCL is suitable for disease classification and works with 97% testing accuracy. GCL can further be used for the classification of more diseases of paddy.

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Generation of synthetic datasets using weighted bayesian association rules in clinical world

Kharya

Soni

Swarnkar

2022

Int. j. inf. tecnol.

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Distribution preserving train-test split directed ensemble classifier for heart disease prediction

Cited by 9 publications

References 18 publications

Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam

Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam

A novel GCL hybrid classification model for paddy diseases

Generation of synthetic datasets using weighted bayesian association rules in clinical world

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