Early Stage Diabetes Prediction via Extreme Learning Machine

TL;DR

This paper introduces a novel extreme learning machine approach for early diabetes prediction using questionnaire data, aiming to facilitate timely diagnosis and prevent severe health complications.

Contribution

The paper presents a new machine learning method specifically designed for early diabetes detection from questionnaire data, addressing late diagnosis issues.

Findings

Effective early prediction of diabetes using the proposed model.

Potential to reduce late-stage diagnoses and associated health risks.

Applicable in resource-limited rural and developing areas.

Abstract

Diabetes is one of the chronic diseases that has been discovered for decades. However, several cases are diagnosed in their late stages. Every one in eleven of the world's adult population has diabetes. Forty-six percent of people with diabetes have not been diagnosed. Diabetes can develop several other severe diseases that can lead to patient death. Developing and rural areas suffer the most due to the limited medical providers and financial situations. This paper proposed a novel approach based on an extreme learning machine for diabetes prediction based on a data questionnaire that can early alert the users to seek medical assistance and prevent late diagnoses and severe illness development.