ArticleJournal of diabetes and metabolic disorders2020
Deep learning approach for diabetes prediction using PIMA Indian dataset.
Article in Journal of diabetes and metabolic disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- A Sophisticated Onscreen Smart Framework for Predicting Diabetes in Remote Healthcare.Diagnostics (Basel, Switzerland) · 2026Article
- Predictive voting model for early diagnosis of diabetes dataset.Frontiers in cardiovascular medicine · 2026Article
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- Sensor-based evaluation of intermittent fasting regimes: a machine learning and statistical approach.International journal of obesity (2005) · 2025Article
- Synergistic feature selection and distributed classification framework for high-dimensional medical data analysis.MethodsX · 2025Article
- MapReduce based big data framework using associative Kruskal poly Kernel classifier for diabetic disease prediction.MethodsX · 2025Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets.Scientific reports · 2025Article
- An Effective Methodology for Diabetes Prediction in the Case of Class Imbalance.Bioengineering (Basel, Switzerland) · 2025Article
- Design of a Low-Complexity Deep Learning Model for Diagnosis of Type 2 Diabetes.Current diabetes reviews · 2025Article
- Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique.Scientific reports · 2024Article
- Analyzing Diabetes Detection and Classification: A Bibliometric Review (2000-2023).Sensors (Basel, Switzerland) · 2024Review
- Supervised Machine Learning-Based Models for Predicting Raised Blood Sugar.International journal of environmental research and public health · 2024Article
- A stacked ensemble machine learning approach for the prediction of diabetes.Journal of diabetes and metabolic disorders · 2024Article
- Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2.PloS one · 2024Review
- Benefit-Risk Assessment of ChatGPT Applications in the Field of Diabetes and Metabolic Illnesses: A Qualitative Study.Clinical medicine insights. Endocrinology and diabetes · 2024Article
- Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics.PloS one · 2024Article
- An intelligent diabetes classification and perception framework based on ensemble and deep learning method.PeerJ. Computer science · 2024Article
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Abstract
purposeInternational Diabetes Federation (IDF) stated that 382 million people are living with diabetes worldwide. Over the last few years, the impact of diabetes has been increased drastically, which makes it a global threat. At present, Diabetes has steadily been listed in the top position as a major cause of death. The number of affected people will reach up to 629 million i.e. 48% increase by 2045. However, diabetes is largely preventable and can be avoided by making lifestyle changes. These changes can also lower the chances of developing heart disease and cancer. So, there is a dire need for a prognosis tool that can help the doctors with early detection of the disease and hence can recommend the lifestyle changes required to stop the progression of the deadly disease.
methodDiabetes if untreated may turn into fatal and directly or indirectly invites lot of other diseases such as heart attack, heart failure, brain stroke and many more. Therefore, early detection of diabetes is very significant so that timely action can be taken and the progression of the disease may be prevented to avoid further complications. Healthcare organizations accumulate huge amount of data including Electronic health records, images, omics data, and text but gaining knowledge and insight into the data remains a key challenge. The latest advances in Machine learning technologies can be applied for obtaining hidden patterns, which may diagnose diabetes at an early phase. This research paper presents a methodology for diabetes prediction using a diverse machine learning algorithm using the PIMA dataset.
resultsThe accuracy achieved by functional classifiers Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT) and Deep Learning (DL) lies within the range of 90-98%. Among the four of them, DL provides the best results for diabetes onset with an accuracy rate of 98.07% on the PIMA dataset. Hence, this proposed system provides an effective prognostic tool for healthcare officials. The results obtained can be used to develop a novel automatic prognosis tool that can be helpful in early detection of the disease.
conclusionThe outcome of the study confirms that DL provides the best results with the most promising extracted features. DL achieves the accuracy of 98.07% which can be used for further development of the automatic prognosis tool. The accuracy of the DL approach can further be enhanced by including the omics data for prediction of the onset of the disease.
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