  The global need for effective disease diagnosis remains substantial, given
the complexities of various disease mechanisms and diverse patient symptoms. To
tackle these challenges, researchers, physicians, and patients are turning to
machine learning (ML), an artificial intelligence (AI) discipline, to develop
solutions. By leveraging sophisticated ML and AI methods, healthcare
stakeholders gain enhanced diagnostic and treatment capabilities. However,
there is a scarcity of research focused on ML algorithms for enhancing the
accuracy and computational efficiency. This research investigates the capacity
of machine learning algorithms to improve the transmission of heart rate data
in time series healthcare metrics, concentrating particularly on optimizing
accuracy and efficiency. By exploring various ML algorithms used in healthcare
applications, the review presents the latest trends and approaches in ML-based
disease diagnosis (MLBDD). The factors under consideration include the
algorithm utilized, the types of diseases targeted, the data types employed,
the applications, and the evaluation metrics. This review aims to shed light on
the prospects of ML in healthcare, particularly in disease diagnosis. By
analyzing the current literature, the study provides insights into
state-of-the-art methodologies and their performance metrics.
