Predicting Tomorrow's Health: Machine Learning for Disease Outbreak and Patient Outcome Forecasting

Authors

  • Purna Chandra Rao Kandimalla, T. Anuradha

Keywords:

Machine Learning, Healthcare Prediction, Decision Trees, Ensemble Methods, Hybrid Model, Data-Driven Medicine.

Abstract

Machine learning may alter healthcare, according to this study. Data from previous healthcare is used to predict disease outbreaks and patient outcomes. The study includes Decision Trees, Neural Networks, SVMs, Ensemble Methods, and a Hybrid model. Studies critically examine the dynamic connection between data-driven methods and medicine. Showing each technique's recall variability between folds helps understand its performance. Additionally, Accuracy connected to Recall for Individual Methods shows each prediction model's strengths and weaknesses. Data synthesis into an overview of Average Accuracy connected to Recall across Folds is crucial to the study. This comprehensive perspective provides healthcare practitioners one predictive model performance indicator. According to the report, healthcare's future depends on model refinement, dataset expansion, and ethics. Recall, Precision, Accuracy, and F1-score contribute to responsible machine learning in healthcare, pointing to patient-centricity, operational efficiency, and ethical integrity.

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Published

06.08.2024

How to Cite

Purna Chandra Rao Kandimalla. (2024). Predicting Tomorrow’s Health: Machine Learning for Disease Outbreak and Patient Outcome Forecasting. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 481–495. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/6892

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Section

Research Article