Variable Weights Neural Network For Diabetes Classification

TL;DR

This paper introduces a novel variable weights neural network approach for early diabetes detection, demonstrating improved accuracy and generalization on a small dataset, which is crucial for medical diagnostics.

Contribution

The paper proposes a new variable weights neural network model specifically designed for diabetes classification, showing significant improvements over previous methods on limited data.

Findings

Improved classification accuracy over state-of-the-art methods

Effective generalization on small datasets

Potential for cost-effective early diagnosis

Abstract

As witnessed in the past year, where the world was brought to the ground by a pandemic, fighting Life-threatening diseases have found greater focus than ever. The first step in fighting a disease is to diagnose it at the right time. Diabetes has been affecting people for a long time and is growing among people faster than ever. The number of people who have Diabetes reached 422 million in 2018, as reported by WHO, and the global prevalence of diabetes among adults above the age of 18 has risen to 8.5%. Now Diabetes is a disease that shows no or very few symptoms among the people affected by it for a long time, and even in some cases, people realize they have it when they have lost any chance of controlling it. So getting Diabetes diagnosed at an early stage can make a huge difference in how one can approach curing it. Moving in this direction in this paper, we have designed a liquid machine learning approach to detect Diabetes with no cost using deep learning. In this work, we have used a dataset of 520 instances. Our approach shows a significant improvement in the previous state-of-the-art results. Its power to generalize well on small dataset deals with the critical problem of lesser data in medical sciences.