Scalable Machine Learning Framework For Patient Outcome Prediction With Cloud-Based Healthcare Data
Keywords:
machine learning, healthcare analytics, scalability, interpretability, cloud computingAbstract
This study offers an expandable machine learning model for predicting patient outcomes that is especially made for the online analysis of medical data. The deductive approach, which is based on interpretivism, incorporates various sources of secondary information into a design that is descriptive in nature. The technical methodology of the framework includes scalability of improvement, machine learning the method deployment, and advanced information preprocessing. The outcomes show that the model's flexibility and predictive accuracy surpass those of the current models. Technical validation confirms that the standards are followed, and robustness testing shows that the system is resilient to a variety of circumstances. Interpretability is one area that could use improvement, according to critical analysis. Increasing model transparency and ongoing improvement are among the suggestions. Subsequent research endeavors to embrace user input, investigate sophisticated explainability strategies, and incorporate novel technologies.
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Copyright (c) 2024 Neha Jain, Maytham N. Meqdad, Vibhav Krashan Chaurasiya, Diwakar Bhardwaj, A. Kakoli Rao, Navneet Kumar, A. Deepak, Anurag Shrivastava
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