Healthcare Recommender System Using Random Forest Classifier
Assistant Professor, Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Trivandrum, India
Dhanush D. Chandran
Student, Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Trivandrum, India
Varsha Sujith Krishna
Student, Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Trivandrum, India
Deanne Maria Rodricks
Student, Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Trivandrum, India
Johann Joshua Knox
Student, Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Trivandrum, India
The increasing reliance on online information for health concerns underscores the need for accurate and reliable medical guidance. Self-diagnosis, often resulting from limited experience and misinformation, poses significant risks to patient safety. To mitigate these risks, we propose the development of a Drug Recommendation System (DRS) leveraging machine learning (ML) techniques, with a particular focus on the Random Forest algorithm. This system aims to analyze extensive medical data to provide personalized medication recommendations, thereby aiding both healthcare professionals and consumers in making informed and cost-effective treatment decisions. The objectives include developing a predictive model using data from various surveys, enhancing the model to analyze patient symptoms and predict the most likely diseases, and suggesting appropriate medications. By integrating dispersed clinical information, the system offers comprehensive insights tailored to user-specific needs while ensuring data validity and privacy. Key considerations include fostering trust in the system’s recommendations through robust validation and a “Doctor-in-the-Loop” approach, which combines human expertise with computational efficiency. The methodology involves collecting and preprocessing data, developing and validating the model using Random Forest and other ML algorithms, integrating Natural Language Processing (NLP) for symptom analysis, and providing corresponding drug treatments. The implementation plan spans data collection, model development, validation, integration, and deployment phases, with an emphasis on continuous improvement. Expected outcomes include a robust system that enhances healthcare decision-making through accurate and personalized drug recommendations, symptom analysis, disease prediction, and corresponding drug suggestions. By focusing on user-specific needs, ensuring data validity and privacy, and fostering trust, the DRS has the potential to significantly improve patient outcomes and healthcare efficiency.