Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges

TL;DR

This paper systematically reviews recent advances in machine learning for disease diagnosis, highlighting algorithms, comparisons, challenges, and their impact on improving diagnostic accuracy across various diseases.

Contribution

It provides a comprehensive survey of machine learning techniques used in disease detection, emphasizing recent developments, comparisons, and ongoing challenges in the field.

Findings

Machine learning enhances disease detection accuracy.

Various algorithms are effective for different diseases.

Challenges include data quality and model interpretability.

Abstract

Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is expanding quickly. Because errors in medical diagnostic systems might lead to seriously misleading medical treatments, major efforts have been made in recent years to improve computer-aided diagnostics applications. The use of machine learning in computer-aided diagnosis is crucial. A simple equation may result in a false indication of items like organs. Therefore, learning from examples is a vital component of pattern recognition. Pattern recognition and machine learning in the biomedical area promise to increase the precision of disease detection and diagnosis. They also support the decision-making process's objectivity. Machine learning provides a practical method for creating elegant and autonomous algorithms to analyze high-dimensional and multimodal bio-medical data. This review article examines machine-learning algorithms for detecting diseases, including hepatitis, diabetes, liver disease, dengue fever, and heart disease. It draws attention to the collection of machine learning techniques and algorithms employed in studying conditions and the ensuing decision-making process.