Disease Predictor Based on Symptoms Using Machine Learning
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Disease prediction based on symptoms is an important task in the medical field which seeks to help in early diagnosis for better treatment of the patient. This research uses machine learning (ML) model to propose a model that would diagnose, given a list of symptoms input by the user. The model review large swathes of the medical records and utilizing the symptoms-disease mapping to propose likely diagnoses. The following predictive models have been suggested for the suggested system – Random Forest, SVM and Decision trees used for modelling to ensure enhanced prediction. The model has been considered in terms of various measures of accuracy – accuracy rate, precision rate, level of recall and F-measure. Furthermore, for improving the interface, friendly interface is developed for people to input the performance techniques and to take the prediction. This research therefore seeks to improve on Clinical Decision-Making tools, lessen Diagnostic Discrepancy, and fast-track intervention thus enhancing healthcare delivery systems.
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