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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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| Paper Title: | Smart Diagnosis: Machine Learning for Early Detection of Diabetes and Heart Disease |
| Authors Name: | Vanshika Gupta |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_204913
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| Published Paper Id: | IJRTI2507002 |
| Published In: | Volume 10 Issue 7, July-2025 |
| DOI: | |
| Abstract: | The global rise in chronic non-communicable diseases—particularly diabetes and cardiovascular diseases (CVDs)—has prompted urgent calls for more effective strategies for early diagnosis and intervention. As of 2021, diabetes affected over 530 million individuals globally, and CVDs accounted for nearly 18 million deaths annually, representing a significant burden on healthcare systems worldwide [Lin, J. et al. (2023), WHO (2021)]. Recent advances in machine learning (ML) have enabled the development of predictive models that analyze large-scale health data to detect these diseases at their earliest stages, often before clinical symptoms manifest. This paper investigates current ML approaches applied to the early detection of diabetes and heart disease, including decision trees, support vector machines, ensemble models, and deep learning architectures. Real-world studies have demonstrated promising results; for example, Roy et al. (2024) achieved 97% accuracy in early type 2 diabetes detection using gene expression data and explainable ML methods [Roy, A. L. et al. (2024)], while Banday et al. (2024) employed a hybrid quantum ML model to enhance coronary heart disease prediction [Banday, M. et al. (2024)]. Furthermore, models using electronic health records and even voice data have shown reliable predictive performance, suggesting strong potential for integration into clinical practice [Abdullah, M. (2025), Klick Labs. (2023)]. By evaluating multiple studies and methodologies, this paper aims to provide a comprehensive overview of ML applications in early diagnostics, identify critical success factors, and outline challenges related to data quality, interpretability, and clinical integration. The findings support the transformative role of machine learning in enabling proactive, data-driven healthcare systems. |
| Keywords: | Machine Learning (ML) , Early Disease Detection , Diabetes Prediction , Heart Disease Diagnosis , Artificial Intelligence in Healthcare , Predictive Modelling , Smart Diagnostics , Health Data Analytics , Chronic Disease Prevention , Clinical Decision Support Systems (CDSS) |
| Cite Article: | "Smart Diagnosis: Machine Learning for Early Detection of Diabetes and Heart Disease", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a5-a11, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507002.pdf |
| Downloads: | 000442 |
| 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 |
| Publication Details: |
Published Paper ID: IJRTI2507002
Registration ID:204913
Published In: Volume 10 Issue 7, July-2025
DOI (Digital Object Identifier):
Page No: a5-a11 Country: Lucknow, Uttar Pradesh, India Research Area: Master of Computer Application Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2507002 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2507002 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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