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Download PDFOpen PDF in browserMachine Learning-Powered Clinical Predictions: from Data to DeploymentEasyChair Preprint 134905 pages•Date: May 31, 2024AbstractThe integration of sophisticated machine learning algorithms into clinical applications has the potential to transform healthcare by providing highly accurate predictive models. This case study focuses on the design, development, and evaluation of clinical predictive applications, with a primary emphasis on machine learning methodologies. The article begins by elucidating the motivation for this initiative, emphasizing the urgent need for advanced predictive models to improve healthcare outcomes. The architectural design and implementation of the application are discussed, highlighting the central role of machine learning at each stage.The study details the comprehensive integration of machine learning algorithms, covering crucial aspects such as data preprocessing, feature extraction, model training, validation, and deployment. Various machine learning techniques, including classification, regression, and clustering, are rigorously analyzed for their effectiveness in predicting clinical outcomes, with a specific focus on pain prediction. The study examines the performance of different models and their respective algorithms, providing a detailed comparison to determine the most effective approaches. The paper concludes with a thorough discussion of the research outcomes, highlighting the significant advantages, potential limitations, and future research directions in the application of machine learning to clinical prediction. This work underscores the transformative power of machine learning algorithms in developing robust, scalable, and highly accurate medical applications, demonstrating a substantial advancement in healthcare predictive capabilities. Keyphrases: APIs, Appointment System, Disease Information, Disease Prediction, MongoDB, NodeJS, ReactJS, Symptoms Checker, logistic regression, machine learning, medication information Download PDFOpen PDF in browser |
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