Novel diabetes classification approach based on CNN-LSTM: Enhanced performance and accuracy
1
Energy, Embedded System, and Data Processing Laboratory, National School of Applied Sciences Oujda (ENSAO), Mohammed First University (UMP), Oujda, 60000, Morocco
Submission date: 2023-07-31
Final revision date: 2024-01-25
Acceptance date: 2024-02-02
Online publication date: 2024-02-11
Publication date: 2024-02-11
Corresponding author
Yassine Ayat
Energy, Embedded System, and Data Processing Laboratory, National School of Applied Sciences Oujda (ENSAO), Mohammed First University (UMP), Oujda, 60000, Morocc
Energy, Embedded System, and Data Processing Laboratory, National School of Applied Sciences Oujda (ENSAO), Mohammed First University (UMP), Oujda, 60000, Morocc
Diagnostyka 2024;25(1):2024112
KEYWORDS
TOPICS
ABSTRACT
This paper deals with the development of a novel approach for diabetes classification based ona Convolutional Neural 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 precision, recall, F1-score, kappa score, and accuracy are employed to assess the performance of each model. The results demonstrate that the CNN- LSTM 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.
FUNDING
This research received no external funding.
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