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13 results for “Diabetes Prediction”
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Research ArticleOpen accessPhrase in the titleA Metaheuristic-Optimized Feature Selection for Early-Stage Diabetes Prediction With SHAP-Guided Insight into Influential AttributesEsmay Azam Rochy, Jannatul Ferdaus, Uzzal Biswas et al.Diabetes is a metabolic disorder that causes elevated blood glucose. This long-term health condition can lead to cardiovascular diseases, stroke, kidney failure, visual impairment, neuropathy, and even death in critical cases. So, a Computer-Aided Diagnostic (CAD) system is necessary to diagnose diabetes automatically. A clinician can utilize a machine learning-based CAD system that automatically diagnoses many people. This paper will … …Journal of Computer Science, Vol. 22, Issue 2 · 24 Feb 2026 · 1,736 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleImproving Diabetes Risk Prediction Using Ensemble Boosting and SMOTE-Based Class BalancingKittipol Wisaeng, Pankom Sriboonlue, Benchalak MuangmeesriAccurate diabetes prediction is vital for early intervention, optimized resource allocation, and minimizing long-term complications. This study presents a comparative evaluation of traditional and advanced machine learning models for diabetes classification using a structured clinical dataset. Seven baseline algorithms were assessed against five advanced ensemble methods: CatBoost, LightGBM, XGBoost, Voting Ensemble, and Stacking Ensemble. To improve algorithm learning, the Synthetic … …Journal of Computer Science, Vol. 22, Issue 1 · 1 Feb 2026 · 1,976 accessesView PDFCite
Research ArticleOpen accessIn the keywordsPredicting Risk of Diabetes using a Model based on Multilayer Perceptron and Features ExtractionFrancesca Fallucchi, Alessandro CabroniDiabetes (diabetes mellitus) is a disease emerging when a person has a high blood sugar level for a prolonged period. In the healthcare context, one of the most important topic is the prevention of the disease. This study relates to diabetes prevention. In particular, it aims to produce random generated datasets according to a rule named Finnish Diabetes Risk Score … …Journal of Computer Science, Vol. 17, Issue 9 · 7 Sep 2021 · 6,580 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAn Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random ForestBindushree Doddasiddavanahalli Channabasavaraju, Udayarani VinayakamurthyData Mining Techniques are used in many areas like banking, healthcare, education because of extracting relevant information from the database. Many algorithms in data mining are used to predict the disease to reduce the patient treatment cost as well as increase the diagnosis accuracy. In this research work, the risk level of diabetic heart disease patient can be predicted using … …Journal of Computer Science, Vol. 16, Issue 1 · 27 Jan 2020 · 7,931 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Integrated Framework to Predict the Strength of New SMILE Using Graph Attention NetworkSandhi Kranthi Reddy, S. V. G. ReddyMachine Learning (ML) and Deep Learning (DL) have significantly advanced various fields, including healthcare, finance, autonomous systems, and scientific research. In healthcare, these technologies have been widely applied to disease prediction, such as cancer and diabetes. However, the challenge of drug resistance persists, creating a need for more effective drugs. Developing new drugs is a complex, expensive, and time-intensive process, … …Journal of Computer Science, Vol. 21, Issue 10 · 25 Nov 2025 · 2,157 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntelliHealth: A Machine Learning Driven Disease Detectionand Diet recommendation SystemAnkita Wadhwan, Priyanka Chawla, Sandeep Kaur, Usha MittalPeople all around the world are afflicted with various ailments. An accurate diagnosis can lower the risk of significant health problems developing, but an inaccurate diagnosis could have adverse implications. In this study, an ensemble-based strategy "IntelliHealth" has been presented to identify disorders of the thyroid, liver, and breast cancer using three machine learning (ML) approaches consisting of Support Vector … …Journal of Computer Science, Vol. 21, Issue 6 · 28 May 2025 · 5,226 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimizing N-linked Glycosylation Site Prediction in Human Proteins with Ensemble Stacking and Cross-ValidationMubina Malik, Jaimin UndaviaThe most frequent post-translational modification of proteins in all territories is glycosylation which impacts many biological activities. The most significant and critical of these modifications is N-linked glycosylation which is associated with various human diseases including diabetes cancer Inflammation Alzheimers and atherosclerosis. This article illustrates recent advances in knowledge of biology that are eventually targeting the computer science sector. Moreover-identification … …Journal of Computer Science, Vol. 20, Issue 12 · 20 Nov 2024 · 4,999 accessesView PDFCite
Research ArticleOpen accessIn the abstractModel Classification for Predicting the Post-Translational Modification (PTM) Glycosylation in Sequence O Using an Extreme Gradient Boosting AlgorithmDamayanti, Sutyarso et al.Post Translational Modification (PTM) is an important mechanism involved in regulating protein function. Post-translational modification refers to the addition of covalent and enzymatic modifications of proteins in protein biosynthesis, which has an important role in modifying protein function and regulating gene expression. One of the post-translational modifications is glycosylation. Glycosylation is the addition of a sugar group to a protein … …Journal of Computer Science, Vol. 20, Issue 7 · 29 Apr 2024 · 5,554 accessesView PDFCite
Research ArticleOpen accessIn the abstractEvaluation of Mir 29c-3p and Mir 31-5p Serum Values in Predicting Nephropathy in Type 2 Diabetic PatientsMona S. Habieb, Nesreen Gamal Elhelbawy, Khaled M. Elzorkany et al.Diabetic Nephropathy (DN) is a prime complication of diabetes with various pathological mechanisms. microRNAs (miRNAs) have emerged as an important factor in the pathogenesis of renal disorders. We aimed to analyze serum miR 29c-3p and miR 31-5p in diabetic patients in relation to albuminuria and eGFR. Also, the correlations between these miRNAs and cystatin C were assessed. This study was … …American Journal of Biochemistry and Biotechnology, Vol. 18, Issue 1 · 17 Feb 2022 · 6,449 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredictive Modeling Applied to Structured Clinical Data Extracted from Electronic Health Records: An Architectural Hypothesis and A First ExperimentAlessandra Pieroni, Alessandro Cabroni, Francesca Fallucchi, Noemi ScarpatoPredictive analysis is one of current important issues in the healthcare context. A lot of patients’ input data can be obtained from their Electronic Health Records. In our research, we propose a general architecture named Health Prediction Architecture. Initially, we consider that data refer to strongly structured health datasets (no free text). Our objectives are related to exploring some problems … …Journal of Computer Science, Vol. 17, Issue 9 · 7 Sep 2021 · 6,571 accessesView PDFCite