International Journal of Science, Engineering and TechnologyAuthors: Hemlata, Ashish Prajapati, Satyam Shrivastava
Abstract: Studies have demonstrated the significance of early death cause interpretation for timely intervention in healthcare globally. The timely implementation of positive corrective measures for healthcare deaths has been correlated with healthcare studies. [1], [7]. Regrettably, the majority of healthcare professionals in resource-poor and neglected areas lack sufficient medical knowledge. Healthcare symptoms are taken at face value and delayed due to unrestricted online clues [2]. Artificial intelligence, or more specifically, predictive machine learning-based decision support systems, have made significant progress in recent years, and that's a positive thing. AI Symptom Assistant Checker is a web application that uses research and machine learning to make initial predictions about a disease by simply inputting its symptoms. It is like any other enabled healthcare AI systems, though in this specific instance we developed a responsive web application, coupled with a flask python web framework and a supervised machine learning model which are Decision Tree, Random Forest, Naive Bayes, Support Vector Machine and Neural Networks which have been used in other research to predict and classify for healthcare. [4], [5], [6]. Well-defined medical datasets are typically used to train and evaluate predictive healthcare models. The standard model for approaches in biomedical data analytics and clinical model development can be found here.
DOI: http://doi.org/10.5281/zenodo.20338735
