Diagnosing diseases rapidly are critical for improving patient outcomes, yet the usual diagnostic pathway causes significant delays due to extended waiting periods between sessions and advanced diagnostic tools. This review paper presents a study on the application of machine learning algorithms for multi-disease identification utilizing crowdsourced symptoms and a web-based platform. In this study, we want to investigate how machine learning algorithms might enhance diagnosis speed and accuracy by predicting diseases based on symptom data. Integrating such a technology into practice workflows can assist healthcare providers in prioritizing testing procedures based on detected risk, allowing for quicker patient diagnosis and treatment. Existing literature on machine learning applications in disease detection Challenges prevailing today for multidisease prediction currently covered within the review. In the end, this application potentially stands to improve patient care access by eliminating complex cases and thereby freeing up clinical expert time for more difficult patients as well as data-driven insights that can lead to better healthcare outcomes.
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