Explorative analysis of human disease-symptoms relations using the Convolutional Neural Network

TL;DR

This study explores how machine learning models, including CNNs and SVMs, can predict diseases from symptoms with high accuracy, emphasizing the importance of unusual symptoms for early diagnosis.

Contribution

The paper demonstrates the effectiveness of CNNs and SVMs in disease prediction from symptoms and highlights the role of unusual symptoms in improving early diagnosis accuracy.

Findings

Machine learning models achieve 98-100% accuracy in disease prediction.

Unusual symptoms significantly enhance prediction accuracy.

Symptom types influence disease predictability.

Abstract

In the field of health-care and bio-medical research, understanding the relationship between the symptoms of diseases is crucial for early diagnosis and determining hidden relationships between diseases. The study aimed to understand the extent of symptom types in disease prediction tasks. In this research, we analyze a pre-generated symptom-based human disease dataset and demonstrate the degree of predictability for each disease based on the Convolutional Neural Network and the Support Vector Machine. Ambiguity of disease is studied using the K-Means and the Principal Component Analysis. Our results indicate that machine learning can potentially diagnose diseases with the 98-100% accuracy in the early stage, taking the characteristics of symptoms into account. Our result highlights that types of unusual symptoms are a good proxy for disease early identification accurately. We also highlight that unusual symptoms increase the accuracy of the disease prediction task.