Learning patterns in hospital readmission

01 / MOTIVATION

The question

How can machine learning identify patterns associated with 30-day hospital readmission in diabetic patients?

02 / METHOD

The approach

Using a public Diabetes Hospital Readmission dataset, the study compares a Random Forest and a deep neural network after data cleaning, normalization, and feature encoding.

The study also investigates influential clinical factors in readmission prediction. The work is published as a chapter in Emerging Trends in Health Informatics, Medical Devices, and Large Language Models.

03 / EVIDENCE

Findings & scope

This is a retrospective predictive-modeling study. The publication contains the model comparison; no deployment or prospective clinical validation is claimed here.