Abstract
In recent years, there has been a substantial increase in the amount of attention paid to the use of machine learning strategies within the area of medical data. Classification and clustering algorithms are two major kinds of machine learning algorithms that are frequently utilized for a variety of tasks in medical data analysis. These algorithms are employed in a variety of different ways. Clustering algorithms are used to group data points that are similar together based on their similarity or distance metrics, while classification algorithms are used to predict the class or category of a new data point based on the patterns learned from labeled data. Classification methods may be found in machine learning software. We present an overview of classification and clustering algorithms for medical data in this article, covering its uses, problems, and future approaches, among other things.