Enhancing Healthcare Decision-Making through artificial intelligence and Machine Learning

Computer science

ABSTRACTIn contemporary times, numerous diseases necessitate early identification for timely and effective treatments; otherwise, they may become incurable and fatal. This underscores the importance of swiftly and accurately analyzing complex medical data, reports, and images. Some abnormalities might elude human recognition, prompting the application of machine learning approaches in healthcare decision-making. These techniques enable critical data analysis, unveiling hidden relationships or abnormalities not readily apparent to humans. Implementing algorithms for such tasks is challenging, and enhancing algorithm accuracy while reducing execution time adds an additional layer of complexity.In the early days, managing extensive medical data spurred the adoption of machine learning in the biological domain. This integration propelled biology and biomedical fields to new heights, uncovering previously unnoticed relationships. The current focus has shifted to precision medicine, where patient treatment considers not only the disease type but also genetics. Continuous modifications and testing of machine learning algorithms aim to improve their performance in analyzing and presenting more accurate healthcare information. Machine learning involvement in healthcare spans from extracting information from medical documents to disease prediction and diagnosis. Medical imaging, greatly enhanced by machine learning algorithms in computational biology, now plays a significant role in disease diagnoses. Additionally, machine learning informs patient care, resource allocation, and research on various treatments. This paper explores diverse machine learning algorithms and approaches utilized in healthcare decision-making, highlighting the integration of machine learning in contemporary healthcare applications. Neural network-based deep learning methods, leveraging the processing power of modern computers, have excelled in computational biology, contributing to their widespread adoption for their predictive accuracy and reliability.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.