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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Enhancing stroke disease classification through machine learning models via a novel voting system by feature

Mahade Hasan¹, Farhana Yasmin², Md Mehedi Hassan³

¹School of Software, Nanjing University of Information Science and Technology, Nanjing, China.

|January 9, 2025

Summary

This summary is machine-generated.

This study developed advanced machine learning models for accurate heart disease prediction. XGBoost achieved 99% accuracy, offering a promising tool for early diagnosis and preventive healthcare.

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Area of Science:

Cardiology
Machine Learning
Data Science

Background:

Heart disease is a major global health concern, driving the need for improved predictive diagnostics.
Existing machine learning models for heart disease prediction often lack sufficient accuracy for clinical application.

Purpose of the Study:

To develop and evaluate advanced machine learning models for accurate heart disease prediction.
To enhance early detection and intervention strategies for cardiovascular diseases.

Main Methods:

Applied nine machine learning algorithms: XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), Gaussian Naive Bayes (NB Gaussian), adaptive boosting, and linear regression.
Utilized feature selection, grid search hyperparameter tuning, and cross-validation to optimize model performance and interpretability.
Developed a novel voting system combined with feature selection for improved heart disease classification.

Main Results:

XGBoost demonstrated superior performance, achieving 99% accuracy, precision, and F1-score, with 98% recall and 100% ROC AUC.
Evaluated models using accuracy, precision, recall, F1-score, and ROC AUC to ensure comprehensive performance assessment.

Conclusions:

The developed XGBoost model offers a highly accurate and reliable approach for early heart disease diagnosis.
This study provides a significant advancement in predictive modeling for preventive cardiovascular healthcare.