Machine Learning for Everyone: Simplifying Healthcare Analytics with BigQuery ML

Machine learning (ML) is transforming healthcare by enabling predictive analytics, personalized treatments, and improved patient outcomes. However, traditional ML workflows require specialized skills, infrastructure, and resources, limiting accessibility for many healthcare professionals. This paper explores how Google Cloud's BigQuery ML simplifies the development and deployment of ML models using SQL, reducing technical barriers. Through a case study on diabetes prediction using the Diabetes Health Indicators Dataset, we evaluate three predictive models: Logistic Regression, Boosted Tree, and Deep Neural Network (DNN). Our results demonstrate that the Boosted Tree model achieves the highest performance, making it highly effective for diabetes prediction. This study highlights BigQuery ML's role in democratizing machine learning by providing a scalable, efficient, and accessible solution for healthcare analytics.