Optimizing Diabetes Prediction with Machine Learning: Model Comparisons and Insights
DOI:
https://doi.org/10.55662/JST.2024.5403Downloads
Keywords:
diabetes prediction, machine learning, Random Forest, Logistic Regression, Support Vector Machine, Gradient Boosting, health indicators, lifestyle factors, model comparison, medical diagnosisAbstract
This study aims to predict diabetes using various machine learning models and compare their performances. The dataset utilized contains health indicators and lifestyle factors from a diverse population. The models evaluated include Random Forest, Logistic Regression, Support Vector Machine (SVM), and Gradient Boosting. Results indicate that Gradient Boosting outperforms other models in terms of accuracy, precision, and recall, making it a robust choice for diabetes prediction. The analysis provides insights into the most significant features contributing to diabetes prediction and highlights the potential of machine learning in medical diagnosis.
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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
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Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.