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Download PDFOpen PDF in browserAnomoly Detection in Medical ImagingEasyChair Preprint 129973 pages•Date: April 11, 2024AbstractThe contemporary landscape of healthcare has been profoundly transformed by the infusion of machine learning techniques, which have heralded a new era of disease prediction and management. This research endeavors to address a critical gap in the existing healthcare paradigm by developing a unified system capable of predicting multiple diseases using a streamlined interface. Focusing primarily on Random Forest, a robust ensemble learning algorithm, and harnessing the power of deep learning, this study pioneers a comprehensive approach to disease forecasting. The study's core objective revolves around the accurate prediction of a spectrum of diseases, ranging from diabetes and heart disease to chronic kidney disease and cancer. Early detection of these ailments is pivotal, as it significantly impacts patient outcomes and healthcare costs. Leveraging Random Forest, a versatile and efficient machine learning algorithm, this research meticulously evaluates its predictive capabilities. By optimizing hyperparameters and fine-tuning the model, the study ensures the highest level of accuracy in disease prognosis. Additionally, the research delves into the realm of deep learning, a subset of machine learning that mimics the intricate neural networks of the human brain. Keyphrases: Medical Diagnosis, Medical Imaging, Pre-detection, disease detection Download PDFOpen PDF in browser |
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