Novel diabetes classification approach based on CNN-LSTM: enhanced performance and accuracy

• Autorzy: Ayat Yassine , Benzekri Wiame , El Moussati Ali , Mir Ismail , Benzaouia Mohammed , El Aouni Abdelaziz

Identyfikatory

DOI

10.29354/diag/183633

• Języki publikacji: EN

Abstrakty

EN

This paper deals with the development of an approach for diabetes classification harnessing ConvolutionalNeural-network (CNN) and a Long-Short-Term-Memory (LSTM) model. The proposed method harnesses the strengths of LSTM and CNN architectures to effectively capture sequential patterns and extract meaningful features from the input data. A comprehensive dataset containing relevant features for diabetes patients is used to train and evaluate the classifiers. Evaluation metrics such as kappa score, F1-score, accuracy, precision, and recall are employed in ordre to assess the performance of each model. The results demonstrate that the CNNLSTM model outperforms other models, including Logistic Regression, Random Forest, SVM, and KNN, achieving an impressive accuracy of 97%. These findings shed light on the effectiveness of the proposed approach in accurately classifying diabetes, resulting in significant advancement in diabetes diagnosis and treatment and opening up exciting possibilities for personalized healthcare.

Słowa kluczowe

EN

diabetes; diabetes classification; dataset balancing; combined model; personalized healthcare

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2024
• Tom: Vol. 25, No. 1
• Strony: art. no. 2024112
• Opis fizyczny: Bibliogr. 51 poz., rys., tab.

Twórcy

autor

Ayat Yassine

• Energy, Embedded System, and Data Processing Laboratory, National School of Applied Sciences Oujda (ENSAO), Mohammed First University (UMP), Oujda, 60000, Morocco

• https://orcid.org/0000-0003-0451-6920

autor

Benzekri Wiame

• Energy, Embedded System, and Data Processing Laboratory, National School of Applied Sciences Oujda (ENSAO), Mohammed First University (UMP), Oujda, 60000, Morocco

• https://orcid.org/0009-0004-7125-7206

autor

El Moussati Ali

• Energy, Embedded System, and Data Processing Laboratory, National School of Applied Sciences Oujda (ENSAO), Mohammed First University (UMP), Oujda, 60000, Morocco

• https://orcid.org/0000-0002-3182-1907

autor

Mir Ismail

• Energy, Embedded System, and Data Processing Laboratory, National School of Applied Sciences Oujda (ENSAO), Mohammed First University (UMP), Oujda, 60000, Morocco

• https://orcid.org/0000-0002-8318-7266

autor

Benzaouia Mohammed

• Energy, Embedded System, and Data Processing Laboratory, National School of Applied Sciences Oujda (ENSAO), Mohammed First University (UMP), Oujda, 60000, Morocco

• https://orcid.org/0000-0002-2583-2124

autor

El Aouni Abdelaziz

• Energy, Embedded System, and Data Processing Laboratory, National School of Applied Sciences Oujda (ENSAO), Mohammed First University (UMP), Oujda, 60000, Morocco

• https://orcid.org/0000-0001-8818-4745

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).