T-RECS: Tumor Treatment Recommendation System

Problem and Motivation 

Cancer is one of the leading causes of death worldwide, and early detection and accurate diagnosis of tumors is critical for effective treatment and improved patient outcomes.

Our recommendation system centralizes relevant information, making it easier for patients to make informed decisions. Tumor conditions often require prompt medical attention and treatment. Having a recommendation system that can quickly connect patients with suitable doctors and specialists can not only help reduce delays and ensure timely access to appropriate care but also greatly enhance the overall patient experience.

Unlike other recommendation systems, our system utilizes a unique feature called Placekey API along machine learning to help patients make informed decisions based on the most current information. In addition to this, our system provides an aggregation of sentiment from all available reviews. By training on core key features we are able to improve the accuracy and relevance of tumor recommendations.

Data Source and Data Science Approach

We utilized two different datasets that were scraped from public sources or acquired via API:

• Data.CMS.Gov Dataset - 16,500+ oncologists across the United States
• Yelp Academic Dataset - 5 gigabyte subset from Yelp. Reviews for businesses in 8 major metropolitan areas across the US and Canada.

We used Amazon S3 to store the dataset and model artifacts, and EC2 instances to set up our Flask UI interface.

Models

• Sentiment Analysis with Placekey API
• Top 5 Best Match by Distance, Rating, and Years of Experience
• HDBScan

Acknowledgements

The project wouldn't be a success without the support and guidance of our 210 Capstone instructors, Dr. Fred Nugen and Alberto Todeschini, and our W210 Capstone peers.