Machine Learning-Based Extensible Analytics Platform for Heterogeneous Medical Data Analysis

The accelerating volume and structural diversity of data generated by healthcare systems, smart medical devices, and enterprise platforms has rendered conventional data analysis pipelines increasingly impractical for nonexpert users. This paper presents an extensible, machine learning-integrated analytics platform designed to enable interactive, code-free analysis of heterogeneous medical datasets through a web-based interface. The system accepts structured and semi-structured data in CSV and Excel formats and executes an automated pipeline encompassing data preprocessing, descriptive statistical analysis, interactive visualization, and machine learning — including regression, clustering, and anomaly detection — without requiring users to possess programming skills. The platform is implemented on a scalable three-tier architecture comprising a React-based frontend, a FastAPI backend for request routing and model orchestration, and a Python-based data processing layer utilizing Pandas, NumPy, Scikit-learn, and Matplotlib. Experimental evaluation across multiple medical datasets demonstrates strong predictive performance — achieving an R² of 0.87 on a clinical regression task and an F1-score of 0.84 on a binary classification task — with end-to-end pipeline latencies consistently below one second. The system advances data-driven decision-making in healthcare, business intelligence, and research environments while maintaining an architecture designed for modular extension.