Comparative Analysis of Machine Learning Models for Disease Prediction
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Keywords:
Machine Learning, Disease Prediction, Comparative Analysis, Health-related Data, Predictive Models, Diagnostic Accuracy, Decision Trees, Support Vector MachinesAbstract
The increasing availability of health-related data and advancements in machine learning techniques have paved the way for the development of predictive models for disease diagnosis and prognosis. This study conducts a comprehensive comparative analysis of various machine learning models applied to disease prediction, aiming to identify the most effective approach for accurate and timely diagnosis. The research focuses on a diverse set of diseases, encompassing both communicable and non-communicable conditions, to ensure the generalizability of the findings. Multiple datasets containing relevant patient information, such as demographic details, medical history, and diagnostic tests, are utilized to train and evaluate the performance of various machine learning algorithms.
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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.
Responsibility and Liability:
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.