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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 10 | Issue 12
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19463
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| Paper Title: | Hybrid Algorithms for Early-Stage Diabetes Prediction |
| Authors Name: | Dr. K. Hari Krishna , Mr. Mamuduru Praveen |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_203160
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| Published Paper Id: | IJRTI2504302 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i4.203160 |
| Abstract: | The principal goal of this task is to combine numerous algorithms and locate strategies to offer an accurate tool for predicting early-degree diabetes. Diabetes is a risky ailment that can significantly damage many organs as soon as it enters the body. Early diabetes detection permits us to take preventative steps, together with normal walks, and avoid excessive sugar intake, which may additionally delay the start of the disorder. Three methodologies are being used collectively to improve prediction accuracy Deep Neural Networks (DNN), Extreme Gradient Boosting (XGBoost), and Particle Swarm Optimization (PSO). PSO makes it viable to improve the DNN and XGBoost fashions' parameters, ensuring the most fulfilling performance all around. The DNN component examines elaborate styles inside the facts of impacted individuals, at the same time, XGBoost reduces mistakes in figuring out those who are at risk for diabetes. We hope to obtain more dependable effects by merging those patterns. Using patient statistics, including blood strain, BMI, and glucose degrees, we will check this technology. The goal is to offer scientific experts a useful early predictive tool that will permit brief and individualized remedies for people at risk of growing diabetes. |
| Keywords: | Early Detection, Diabetes Prediction, Particle Swarm Optimization, Deep Neural Networks, XGBoost, and Hybrid Model. |
| Cite Article: | "Hybrid Algorithms for Early-Stage Diabetes Prediction", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.d6-d16, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504302.pdf |
| Downloads: | 000325 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2504302
Registration ID:203160
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i4.203160
Page No: d6-d16 Country: N.T.R District., Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504302 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504302 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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