Publication Date: 2023/09/16
Abstract: The healthcare industry is increasingly concerned about medical errors, which are the leading cause of death worldwide and also compromise patient safety. This medical error is even more serious in developing countries where healthcare is not supported by technology.Therefore, this study aims to assess physicians’ perceptions towards electronic prescription implementation.Accurate and on-time analysis of any health-related problem is important for the prevention and treatment of theillness. The traditional way of diagnosis may not be sufficientin the case of a serious ailment. Developing a medical diagnosis system based on machine learning (ML) algorithms for prediction of any disease can help in a more accurate diagnosis than the conventional method. We have designed a disease prediction system using multiple ML algorithms. The dataset used had more than 41 diseases for processing. Based on the symptoms, age, and gender of an individual, the diagnosissystem givesthe output as the disease that the individual might be suffering from. The Naïve Bayes algorithm gave the best results as compared to the other algorithms. The accuracy of the Naïve Bayes algorithm for the prediction was 99%. Our diagnosis model can act as a doctor for the early diagnosis of a disease to ensure the treatment can take place on time and lives can be saved.
Keywords: Healthcare, Prescription System, Treatment, Diagnosis, Machine Learning, Symptoms, Naive Bayes.
DOI: https://doi.org/10.5281/zenodo.8351030
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23MAY572.pdf
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