Comparison of LSTM and GRU in Predicting the Number of Diabetic Patients

Rochman, Eka Mala Sari; Miswanto,; Suprajitno, Herry; Rachmad, Aeri; Nindyasari, Ratih; Rachman, Fika Hastarita

doi:10.1109/itis57155.2022.10009036

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…A comparison between LSTM and GRU models [8,13] revealed that GRU might offer better accuracy in certain contexts, suggesting that the choice of model architecture could be crucial depending on the specific nature of the diabetes data being analysed. The BiLSTM with Attention model [9], which employed EHRs for prediction, reportedly achieved higher precision and recall than traditional methods, although exact figures were not specified, highlighting the potential of attention mechanisms in enhancing LSTM model performance.…”

Section: Discussionmentioning

confidence: 99%

“…This model indicated the potential of LSTM in early detection and diagnosis of diabetes. Rochman et al [8] took a comparative approach, analysing LSTM against Gated Recurrent Unit (GRU) models. Their study highlighted the nuanced differences between these architectures, contributing to a better understanding of their respective strengths and limitations in diabetes prediction.…”

Section: Related Studiesmentioning

confidence: 99%

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Leveraging 7-layers LSTM for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach

Al Sadi,

Balachandran

2024

Preprint

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This study develops a 7-layer Long Short-Term Memory (LSTM) model to enhance early diabetes detection in Oman, aligning with the theme of 'Artificial Intelligence in Healthcare'. The model focuses on addressing the increasing prevalence of Type 2 diabetes, projected to impact 23.8% of Oman's population by 2050. It employs LSTM neural networks to manage factors contributing to this rise, including obesity and genetic predispositions, and aims to bridge the gap in public health awareness and prevention. The model's performance is evaluated through various metrics. It achieves an accuracy of 99.40%, specificity and sensitivity of 100% for positive cases, a recall of 99.34% for negative cases, an F1 score of 96.24%, and an AUC score of 94.51%. These metrics indicate the model's capability in diabetes detection. The implementation of this LSTM model in Oman's healthcare system is proposed to enhance early detection and prevention of diabetes. This approach reflects an application of AI in addressing a significant health concern, with potential implications for similar healthcare challenges globally.st diagnostic capabilities, representing a significant leap forward in healthcare technology in Oman.

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Section: Discussionmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Leveraging 7-layers LSTM for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach

Al Sadi,

Balachandran

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…A comparison between LSTM and GRU models [8,13] revealed that GRU might offer better accuracy in certain contexts, suggesting that the choice of model architecture could be crucial depending on the specific nature of the diabetes data being analysed. The BiLSTM with the Attention model [9], which employed EHRs for prediction, reportedly achieved higher precision and recall than traditional methods, although exact figures were not specified, highlighting the potential of attention mechanisms in enhancing LSTM model performance.…”

Section: Discussionmentioning

confidence: 99%

“…This model indicated the potential of LSTM in the early detection and diagnosis of diabetes. Rochman et al [8] took a comparative approach, analysing LSTM against Gated Recurrent Unit (GRU) models. Their study highlighted the nuanced differences between these architectures, contributing to a better understanding of their respective strengths and limitations in diabetes prediction.…”

Section: Related Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging a 7-Layer Long Short-Term Memory Model for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach

Al Sadi,

Balachandran

2024

Bioengineering

View full text Add to dashboard Cite

This study develops a 7-layer Long Short-Term Memory (LSTM) model to enhance early diabetes detection in Oman, aligning with the theme of ‘Artificial Intelligence in Healthcare’. The model focuses on addressing the increasing prevalence of Type 2 diabetes, projected to impact 23.8% of Oman’s population by 2050. It employs LSTM neural networks to manage factors contributing to this rise, including obesity and genetic predispositions, and aims to bridge the gap in public health awareness and prevention. The model’s performance is evaluated through various metrics. It achieves an accuracy of 99.40%, specificity and sensitivity of 100% for positive cases, a recall of 99.34% for negative cases, an F1 score of 96.24%, and an AUC score of 94.51%. These metrics indicate the model’s capability in diabetes detection. The implementation of this LSTM model in Oman’s healthcare system is proposed to enhance early detection and prevention of diabetes. This approach reflects an application of AI in addressing a significant health concern, with potential implications for similar healthcare challenges relating to globally diagnostic capabilities, representing a significant leap forward in healthcare technology in Oman.

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