Diabetes diagnosis using a hybrid CNN LSTM MLP ensemble
Abstract Diabetes is a chronic condition brought on by either an inability to use insulin effectively or a lack of insulin produced by the body. If left untreated, this illness can be lethal to a person. Diabetes can be treated and a good life can be led with early diagnosis. The conventional method...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12151-y |
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| author | Yanmin Fan |
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| author_sort | Yanmin Fan |
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| description | Abstract Diabetes is a chronic condition brought on by either an inability to use insulin effectively or a lack of insulin produced by the body. If left untreated, this illness can be lethal to a person. Diabetes can be treated and a good life can be led with early diagnosis. The conventional method of identifying diabetes utilizing clinical and physical data is laborious, hence an automated method is required. An ensemble deep learning model is presented in this research for the diagnosis of diabetes which includes three steps. Preprocessing is the first step, which includes cleaning, normalizing, and organizing the data so that it can be fed into deep learning models. The second step involves employing two neural networks to retrieve features. Convolutional neural network (CNN) is the first neural network utilized for extracting the spatial characteristics of the data, while Long Short-Term Memory (LSTM) networks—more specifically, an LSTM Stack—are used to comprehend the time-dependent flow of the data based on medical information from patients. The last step is combining the two feature sets that the CNN and LSTM models have acquired to create the input for the MLP (Multi-layer Perceptron) classifier. To diagnose sickness, the MLP model serves as a meta-learner to combine and convert the data from the two feature extraction algorithms into the target variable. According to the implementation results, the suggested approach outperformed the compared approaches in terms of average accuracy and precision, achieving 98.28% and 0.99%, respectively, indicating a very great capacity to identify diabetes. |
| format | Article |
| id | doaj-art-dcc38cfd0bf6430396a0bcc30a262c93 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dcc38cfd0bf6430396a0bcc30a262c932025-08-20T03:43:15ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-12151-yDiabetes diagnosis using a hybrid CNN LSTM MLP ensembleYanmin Fan0Faculty of Information Engineering and Automation, Kunming University of Science and TechnologyAbstract Diabetes is a chronic condition brought on by either an inability to use insulin effectively or a lack of insulin produced by the body. If left untreated, this illness can be lethal to a person. Diabetes can be treated and a good life can be led with early diagnosis. The conventional method of identifying diabetes utilizing clinical and physical data is laborious, hence an automated method is required. An ensemble deep learning model is presented in this research for the diagnosis of diabetes which includes three steps. Preprocessing is the first step, which includes cleaning, normalizing, and organizing the data so that it can be fed into deep learning models. The second step involves employing two neural networks to retrieve features. Convolutional neural network (CNN) is the first neural network utilized for extracting the spatial characteristics of the data, while Long Short-Term Memory (LSTM) networks—more specifically, an LSTM Stack—are used to comprehend the time-dependent flow of the data based on medical information from patients. The last step is combining the two feature sets that the CNN and LSTM models have acquired to create the input for the MLP (Multi-layer Perceptron) classifier. To diagnose sickness, the MLP model serves as a meta-learner to combine and convert the data from the two feature extraction algorithms into the target variable. According to the implementation results, the suggested approach outperformed the compared approaches in terms of average accuracy and precision, achieving 98.28% and 0.99%, respectively, indicating a very great capacity to identify diabetes.https://doi.org/10.1038/s41598-025-12151-yDiabetes diseaseEnsemble learningDeep neural networksConvolutional neural networkLong short-term memory |
| spellingShingle | Yanmin Fan Diabetes diagnosis using a hybrid CNN LSTM MLP ensemble Scientific Reports Diabetes disease Ensemble learning Deep neural networks Convolutional neural network Long short-term memory |
| title | Diabetes diagnosis using a hybrid CNN LSTM MLP ensemble |
| title_full | Diabetes diagnosis using a hybrid CNN LSTM MLP ensemble |
| title_fullStr | Diabetes diagnosis using a hybrid CNN LSTM MLP ensemble |
| title_full_unstemmed | Diabetes diagnosis using a hybrid CNN LSTM MLP ensemble |
| title_short | Diabetes diagnosis using a hybrid CNN LSTM MLP ensemble |
| title_sort | diabetes diagnosis using a hybrid cnn lstm mlp ensemble |
| topic | Diabetes disease Ensemble learning Deep neural networks Convolutional neural network Long short-term memory |
| url | https://doi.org/10.1038/s41598-025-12151-y |
| work_keys_str_mv | AT yanminfan diabetesdiagnosisusingahybridcnnlstmmlpensemble |