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Sentiment analysis using healthcare data
1 Department of Statistics, Faculty of Science and Technology, Federal Polytechnic Ugep, Cross River, Nigeria.
2 College of Professional Studies, Analytics, Northeastern University Toronto, Canada.
3 Department of Statistics, Faculty of Science, Ekiti State University, Ado-Ekiti, Ekiti State, Nigeria.
Research Article
International Journal of Science and Research Archive, 2024, 13(02), 3143-3148.
Article DOI: 10.30574/ijsra.2024.13.2.2476
Publication history:
Received on 08 November 2024; revised on 17 December 2024; accepted on 19 December 2024
Abstract:
Healthcare sentiment analysis focuses on diagnosing healthcare-related issues that people have discovered. To create rules and changes that could directly address patients' issues by considering their input. Machine learning techniques analyze millions of review documents and conclude them towards an efficient and accurate decision. This study trained the health data using different machine-learning algorithms: Multinomial Naïve Bayes, Random Forest, Decision Trees, K-nearest neighbor, and Support Vector Machine and compared the accuracy using different evaluation metrics. The study used a drug review dataset from the UCI machine-learning repository. It was separated so that 70% of the data was used as a training dataset for the ML models. The remaining 30% of the data forms the test dataset used to evaluate the trained ML models.
Based on the evaluation metrics, the random forest has the highest accuracy (89.4%) and R squared (0.501), and the lowest MSE (10.5) and RSME (0.324). The study concluded that the random forest classifier is the optimal model for predicting healthcare data while KNN has the lowest accuracy. It is recommended government health ministry and healthcare facilities use online health data to create policies that will directly address these public health issues, allowing patients to directly address their concerns to higher authorities without having to go through arduous procedures
Keywords:
Sentiment analysis; Machine Learning; Online Health data; Social Media
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
