Importance Of Continuous Improvement of Machine Learning Algorithms From A Health Care Management And Management Information Systems Perspective

In modern times, researchers in the healthcare sector increasingly acknowledge the significance of data analysis. In the healthcare sector, data can be accessed from various means such as sensor data, Clinical data, and Omics data. Data from various wireless sensor devices and the wearable device are a form of sensor data. Data from the health records that store patient's records during treatment are a form of clinical data. A high dimensional data that contains proteome data types, transcriptome, and Genome are a form of Omics data. Raw data can be very difficult to handle manually, for this reason, the emergence of machine learning proved to be a significant tool for data analysis. The prediction and precision of healthcare data accurately are now more visible because of the various statistical techniques and advanced algorithms that machine learning employs. There are different types of algorithms in machine learning such as the hybrid model, reinforced learning, unsupervised learning, and supervised learning that are utilized for data analysis. In this paper, the description of various types of machine learning algorithms and the analysis of healthcare data are surveyed using the machine learning algorithms.