Novel diabetes classification approach based on CNN-LSTM: Enhanced performance and accuracy
More details
Hide details
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
Diagnostyka 2024;25(1):2024112
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.
This research received no external funding.
Diabetes Statistics. Center for Diabetic Empowerment Education.
Meneghetti L, Terzi M, Del Favero S, Susto GA, Cobelli C. Data-Driven Anomaly Recognition for Unsupervised Model-Free Fault Detection in Artificial Pancreas. IEEE Transactions on Control Systems Technology 2020; 28(1): 33–47.
Warshaw H, Isaacs D, MacLeod J. The Reference Guide to Integrate Smart Insulin Pens Into Data-Driven Diabetes Care and Education Services. The Diabetes Educator 2020; 46(4_suppl): 3S-20S.
Ellahham S. Artificial Intelligence: The Future for Diabetes Care. The American Journal of Medicine 2020; 133(8): 895–900.
Ibrahim MS, Saber S. Machine Learning and Predictive Analytics: Advancing Disease Prevention in Healthcare. Journal of Contemporary Healthcare Analytics 2023; 7(1), 53-71.
Behera A. Use of artificial intelligence for management and identification of complications in diabetes. Clinical Diabetology 2021; 10(2): 221–5.
Polat K, Güneş S, Arslan A. A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine. Expert Systems with Applications 2008; 34(1): 482–7.
Polat K, Güneş S. An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing 2007; 17(4): 702–10.
Kannadasan K, Edla DR, Kuppili V. Type 2 diabetes data classification using stacked autoencoders in deep neural networks. Clinical Epidemiology and Global Health 2019; 7(4): 530–5.
Caliskan A, Yuksel ME, Badem H, Basturk A. Performance improvement of deep neural network classifiers by a simple training strategy. Engineering Applications of Artificial Intelligence 2018; 67: 14–23.
Naz H, Ahuja S. Deep learning approach for diabetes prediction using PIMA Indian dataset. Journal of Diabetes & Metabolic Disorders 2020; 19(1): 391–403.
Zhu C, Idemudia CU, Feng W. Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques. Informatics in Medicine Unlocked 2019; 17: 100179.
Mercaldo F, Nardone V, Santone A. Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning Techniques. Procedia Computer Science 2017; 112: 2519–28.
Qawqzeh YK, Bajahzar S, Jemmali M, Otoom MM, Thaljaoui A. Classification of diabetes using photoplethysmogram (PPG) waveform analysis: logistic regression modeling. BioMed Research International 2020; Article ID 3764653.
Tafa Z, Pervetica N, Karahoda B. An intelligent system for diabetes prediction. 2015; 378–82.
Hussain A, Naaz S. Prediction of Diabetes Mellitus: Comparative Study of Various Machine Learning Models. 2021; 103–15.
Whig P, Gupta K, Jiwani N, Jupalle H, Kouser S, Alam N. A novel method for diabetes classification and prediction with Pycaret. Microsystem Technologies 2023; 29(10): 1479–87.
Hasan MdK, Alam MdA, Das D, Hossain E, Hasan M. Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers. IEEE Access 2020; 8: 76516–31.
Mamuda M, Sathasivam S. Predicting the survival of diabetes using neural network. 2017; 1870: 040046.
Malasinghe LP, Ramzan N, Dahal K. Remote patient monitoring: a comprehensive study. Journal of Ambient Intelligence and Humanized Computing 2019; 10(1): 57–76.
Maniruzzaman Md, Rahman MdJ, Ahammed B, Abedin MdM. Classification and prediction of diabetes disease using machine learning paradigm. Health Information Science and Systems 2020; 8(1): 7.
Jackins V, Vimal S, Kaliappan M, Lee MY. AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes. The Journal of Supercomputing 2021; 77(5): 5198–219.
Sneha N, Gangil T. Analysis of diabetes mellitus for early prediction using optimal features selection. Journal of Big Data 2019; 6(1): 13.
Mohapatra SK, Swain JK, Mohanty MN. Detection of Diabetes Using Multilayer Perceptron. International Conference on Intelligent Computing and Applications 2019; 109–16.
Sisodia D, Sisodia DS. Prediction of Diabetes using Classification Algorithms. Procedia Computer Science 2018; 132: 1578–85.
Orabi KM, Kamal YM, Rabah TM. Early Predictive System for Diabetes Mellitus Disease. Proceedings of the Industrial Conference on Data Mining 2017; 420–427.
Alade OM, Sowunmi OY, Misra S, Maskeliūnas R, Damaševičius R. A Neural Network Based Expert System for the Diagnosis of Diabetes Mellitus. Information Technology Science 2018; 14–22.
Kirasich K, Smith T, Sadler B. Random forest vs logistic regression: binary classification for heterogeneous datasets. SMU Data Science Review 2018; 1(3): 9.
Xu Y, Klein B, Li G, Gopaluni B. Evaluation of logistic regression and support vector machine approaches for XRF based particle sorting for a copper ore. Minerals Engineering 2023; 192: 108003.
Shehadeh A, Alshboul O, Al Mamlook RE, Hamedat O. Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression. Automation in Construction 2021; 129: 103827.
Basha SM, Rajput DS. Chapter 9 - Survey on Evaluating the Performance of Machine Learning Algorithms: Past Contributions and Future Roadmap. Deep Learning and Parallel Computing Environment for Bioengineering Systems 2019; 153–64.
Taha AA, Malebary SJ. An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine. IEEE Access 2020; 8: 25579–87.
Demir S, Sahin EK. An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost. Neural Computing and Applications 2023; 35(4): 3173–90.
Wang, Alex X, Chukova SS, Nguyen BP. Ensemble k-nearest neighbors based on centroid displacement. Information Sciences 2023; 629: 313-323.
Awad M, Khanna, R. Support vector machines for classification. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers 2015; 39-66.
Nikhar S, Karandikar AM. Prediction of heart disease using machine learning algorithms. International Journal of Advanced Engineering, Management and Science 2016; 2(6): 239484.
Nam Y, Lee C. Cascaded Convolutional Neural Network Architecture for Speech Emotion Recognition in Noisy Conditions. Sensors 2021; 21(13): 4399.
Soni A, Al-Sarayreh M, Reis MM, Brightwell G. Hyperspectral imaging and deep learning for quantification of Clostridium sporogenes spores in food products using 1D- convolutional neural networks and random forest model. Food Research International 2021; 147: 110577.
Chaerun NE, Yean-Der K. Comparative assessment to predict and forecast water-cooled chiller power consumption using machine learning and deep learning algorithms. Sustainability 2021; 13(2): 744.
Patel E, Kushwaha DS. A hybrid CNN-LSTM model for predicting server load in cloud computing. The Journal of Supercomputing 2022; 78(8): 1–30.
Kim TY, Cho SB. Predicting residential energy consumption using CNN-LSTM neural networks. Energy 2019; 182: 72–81.
Zhu J, Chen H, Ye W. Classification of Human Activities Based on Radar Signals using 1D-CNN and LSTM. 2020 IEEE International Symposium on Circuits and Systems (ISCAS) 2020; 1–5.
Patil BM, Joshi RC, Toshniwal D. Hybrid prediction model for Type-2 diabetic patients. Expert Systems with Applications 2010; 37(12): 8102–8.
Marcano-Cedeño A, Torres J, Andina D. A prediction model to diabetes using artificial metaplasticity. International Work-Conference on the Interplay Between Natural and Artificial Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
Ahmad A, Mustapha A, Zahadi ED, Masah N, Yahaya NY. Comparison between Neural Networks against Decision Tree in Improving Prediction Accuracy for Diabetes Mellitus. Digital Information Processing and Communications 2011; 537–45.
Kahramanli H, Allahverdi N. Design of a hybrid system for the diabetes and heart diseases. Expert Systems with Applications 2008; 35(1): 82–9.
Priyadarshini R, Dash N, Mishra R. A Novel approach to predict diabetes mellitus using modified Extreme learning machine. 2014 International Conference on Electronics and Communication Systems (ICECS) 2014; 1–5.
Wu H, Yang S, Huang Z, He J, Wang X. Type 2 diabetes mellitus prediction model based on data mining. Informatics in Medicine Unlocked 2018; 10: 100–7.
Yassine A, Ali EM, Ismail M. Telemedicine in the Era of Covid-19: Teleconsultation Architecture Platform. Proceedings of the 3rd International Conference on Electronic Engineering and Renewable Energy Systems 2023; 347–56.
Ayat Y, El Moussati A, Benzaouia M, Mir I. New Topology of WSN for Smart Irrigation with Low Consumption and Long Range. In International Conference on Digital Technologies and Applications 2023; 221-231.
Ismail M, Anas B, Yassine A, Mohammed B. Improved control technique based on neural network for AC-Chopper of railway substations. 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) 2022; 1-6.
Journals System - logo
Scroll to top