Abstract
Health informatics is an interdisciplinary field in the computer and health sciences. Health informatics, which enables the effective use of medical information, has the potential to reduce both the cost and the burden of healthcare workers during the pandemic process. Using the machine learning algorithms support vector machines, naive bayes, k-nearest neighbor, and C4.5 algorithms, a model performance evaluation was performed to identify the algorithm that will show the highest performance for the prediction of the disease. Three separate training and test datasets were created 70% - 30%, 75% - 25%, and 80% - 20%, respectively. The implementation phase of the study was carried out by following the CRISP-DM steps, and the analyses were made using the R language. By examining the model performance evaluation criteria, the findings show that the C4.5 algorithm showed the best performance with 70% training dataset.
TopBackground
Machine Learning
Machine learning, a sub-branch of artificial intelligence (AI), is a system that investigates the study and construction of algorithms that can make predictions on data for multidimensional biomedical and mathematical data analysis (Alwabel & Zeng, 2021). Machine learning algorithms work on the principle of creating a model to make data-based predictions and decisions based on existing examples, instead of following program instructions one-to-one like rule-based algorithms (Naqa & Murphy, 2015). The machine learning process, which starts with the collection of data from different sources, generates models based on the training data received by the algorithms. In other words, when the machine learning algorithm is trained with data, the machine learning model emerges. For example, a predictive algorithm creates a predictive model (Data Science and Machine Learning, 2021). Machine learning algorithms are basically classified in four ways as supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
Key Terms in this Chapter
Machine Learning: A sub-branch of artificial intelligence and makes data-based predictions using existing data.
Training Dataset: The dataset used to train the algorithm.
Classification: The process of determining which of the existing classes a sample belongs to.
Algorithm: The path followed to solve a problem or achieve a goal.
Decision Tree: A classification method that has the appearance of a tree structure.
Supervised Learning: Making predictions for samples that the learning model has not evaluate before by taking a set of labeled samples as training data.
Test Dataset: Used to evaluate the performance of the model trained with the training dataset.