Introduction

Heya, i built a Hospital lenght of stay predictor web app in which the user selects the conditions of his or her predicament in the hospital and the model predicts how long they stay at the hospital.

its a full-stack machine learning web application that predicts patient hospital stay duration using XGBoost. Features interactive map-based hospital selection across New York State, a 13-feature clinical assessment form, and real-time predictions with risk factor analysis. Built with Flask, JavaScript, Leaflet.js, and deployed on Render.

Overview

This predictor dashboard was built using the Hospital Inpatient Discharges (SPARCS De-Identified) 2017 dataset released by the New York State Department of Health. The platform addresses a real-world scenario: when patients or their relatives visit hospitals, they can predict the expected length of stay based on clinical, demographic, and facility factors.

Demo

Here is a live demo on web app on Render

Check out the Github Repo here pls comment,star and fork

metrosmash / Hospital_LOS_Predictor

A full-stack machine learning web application that predicts patient hospital stay duration using XGBoost. Features interactive map-based hospital selection across New York State, a 13-feature clinical assessment form, and real-time predictions with risk factor analysis. Built with Flask, JavaScript, Leaflet.js, and deployed on Render

🏥 Hospital Length of Stay Predictor

An interactive web-based healthcare analytics platform that predicts hospital Length of Stay (LOS) using machine learning. Built with real-world data from 2.3+ million hospital discharges across New York State, this tool helps patients, families, and healthcare administrators make informed decisions about hospital admissions.

📋 Overview

This predictor dashboard was built using the Hospital Inpatient Discharges (SPARCS De-Identified) 2017 dataset released by the New York State Department of Health. The platform addresses a real-world scenario: when patients or their relatives visit hospitals, they can predict the expected length of stay based on clinical, demographic, and facility factors.

Why This Matters

For Patients & Families: Plan work leave, childcare, and financial arrangements
For Hospitals: Optimize bed management and discharge planning
For Healthcare Administrators: Resource allocation and capacity forecasting
For Researchers: Explore social determinants of health and disparities in care