Multiple Disease Prediction System using Machine Learning
DOI:
https://doi.org/10.54060/a2zjournals.jieee.110Keywords:
Machine Learning, Python, Random Forest, Support Vector MachineAbstract
Machine learning advancements have spurred a revolution in healthcare by making it possible to create prediction models for early disease diagnosis. This report introduces the Multiple Disease Prediction System (MDPS), a state-of-the-art approach that uses machine learning to forecast the likelihood of several diseases based on patient data, including medical history, lifestyle, and demographics. The MDPS addresses the growing difficulties in healthcare by focusing on the early detection of multiple diseases. Some of its crucial components include data preparation, feature selection, model training and disease Detection. Despite advantages like early detection and cost savings, dealing with data privacy, model interpretability, and continuous improvements is essential for MDPS's ethical and efficient usage in healthcare. As a result, MDPS has a significant potential to enhance public health and minimize the difficulties associated with chronic illnesses.
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Copyright (c) 2020 Ahad Rehman, Shikha Singh, Vineet Singh, Bramah Hazela
This work is licensed under a Creative Commons Attribution 4.0 International License.
