# Machine learning for high-risk hospitalization prediction in outpatient individuals with diabetes at a tertiary hospital

## TL;DR

This study uses machine learning to predict which diabetes patients are at high risk of hospitalization, helping healthcare providers monitor them more closely.

## Contribution

A novel combination of XGBoost and Instance Hardness Threshold models improves hospitalization prediction for diabetes patients.

## Key findings

- The XGBoost and Instance Hardness Threshold model achieved 93% sensitivity in predicting hospitalization events.
- Key predictors include outpatient visit frequency, kidney function changes, and age groups under 24 and 65-70 years old.

## Abstract

To characterize, via a predictive model using real-world data, patients with diabetes with a
heightened probability of hospitalization.

At the Endocrinology Unit of a tertiary public hospital in Rio Grande do Sul, Brazil, a
retrospective cohort study analyzed initial consultations from January 1, 2015, to December
31, 2017, focusing on 617 patients with diabetes. Within this group, 82.98% (512 patients) did
not require hospitalization, while 17.02% (105 patients) were hospitalized at least once.
Multiple machine learning algorithms were tested, and the combination of XGBoost and Instance
Hardness Threshold models displayed the best predictive performance. The SHapley Additive
exPlanations method was used for result interpretation.

The most optimal performance was observed by combining the XGBoost and Instance Hardness
Threshold models, resulting in the highest sensitivity (0.93) in accurately classifying
hospitalization events, with an acceptable area under the curve of 0.72. Key predictive
features included the number of outpatient visits, amplitude of estimated glomerular
filtration rate, and age (individuals below 24 years old and between 65 to 70 years old had
higher hospitalization likelihood).

The proposed model demonstrated high predictive capability and may help to identify patients
with diabetes who should be more closely monitored to reduce their risk of
hospitalization