Involvement of Machine Learning Tools in Healthcare Decision Making

Area of Science:

• Computational Biology
• Biomedicine
• Artificial Intelligence in Healthcare

Background:

• Early disease detection is crucial for effective treatment, necessitating accurate and rapid analysis of complex medical data.
• Human limitations in recognizing subtle abnormalities in medical data highlight the need for advanced analytical tools.
• Machine learning (ML) has become integral to healthcare for analyzing vast datasets, uncovering hidden patterns, and supporting clinical decision-making.

Purpose of the Study:

• To explore the application of various machine learning algorithms in healthcare decision-making.
• To discuss the current involvement of ML in diverse healthcare applications, including medical imaging and precision medicine.
• To highlight the performance and impact of neural network-based deep learning in computational biology.

Main Methods:

• Review of machine learning algorithms and approaches used in healthcare decision-making.
• Analysis of ML integration in medical data analysis, information extraction, and disease prediction.
• Examination of deep learning methods, particularly neural networks, in computational biology applications.

Main Results:

• Machine learning significantly enhances the accuracy and speed of medical data analysis, aiding in early disease identification.
• Deep learning, especially neural networks, demonstrates high predictive accuracy and reliability in computational biology, supported by advanced computing power.
• ML is increasingly applied across healthcare, from medical imaging analysis to patient care, resource allocation, and treatment research.

Conclusions:

• Computational biology and biomedicine-based healthcare decisions are now heavily reliant on machine learning algorithms.
• Machine learning, particularly deep learning, is indispensable for advancing precision medicine and improving diagnostic capabilities.
• The integration of AI and ML is transforming healthcare by enabling more accurate, efficient, and personalized patient care.