Efficient Disease Diagnosis Platform: Multi-Model Solution for Early Health Condition Detection

This paper presents a multi-disease prediction system that leverages machine learning to enable the early diagnosis of five conditions: diabetes, heart disease, Parkinson's, breast cancer, and pneumonia. Unlike single-disease models, this system integrates diverse diagnostic tasks into a unifie...

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Bibliographic Details
Published inInternational Conference on Signal Processing and Communication (Online) pp. 363 - 368
Main Authors Beniwal, Ruby, Kalra, Shruti, Nisha, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.02.2025
Subjects
Accuracy
Breast cancer
Classification algorithms
Convolutional Neural Network (CNN)
Convolutional neural networks
Healthcare diagnostics
Machine learning
Multi-disease prediction
Pneumonia
Random forests
Signal processing
Streaming media
Support vector machines
X-ray imaging
Online AccessGet full text
ISSN2643-444X
DOI10.1109/ICSC64553.2025.10967730

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Summary:This paper presents a multi-disease prediction system that leverages machine learning to enable the early diagnosis of five conditions: diabetes, heart disease, Parkinson's, breast cancer, and pneumonia. Unlike single-disease models, this system integrates diverse diagnostic tasks into a unified framework, delivering high accuracy across multiple diseases. Classification algorithms Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and XGBoostare employed to identify the optimal model for each condition, achieving up to 97.66% accuracy in breast cancer detection. For pneumonia, a 14-layer Convolutional Neural Network (CNN) is used to analyze chest X-ray images effectively. The systems single-user interface facilitates rapid, data-driven predictions, streamlining clinical workflows and supporting proactive disease management.
ISSN:2643-444X
DOI:10.1109/ICSC64553.2025.10967730