In the literature, most of the related studies utilised one or more machine learning algorithms for a particular disease prediction. With the increasing availability of electronic health data, more robust and advanced computational approaches such as machine learning have become more practical to apply and explore in disease prediction area. This practice often leads to unwanted biases, errors and high expenses, and negatively affects the quality of service provided to patients. Traditionally, standard statistical methods and doctor’s intuition, knowledge and experience had been used for prognosis and disease risk prediction. This made the principal contribution of this study (i.e., comparison among different supervised machine learning algorithms) more accurate and comprehensive since the comparison of the performance of a single algorithm across different study settings can be biased and generate erroneous results. More specifically, this article considered only those studies that used more than one supervised machine learning algorithm for a single disease prediction in the same research setting. In making comparisons among different supervised machine learning algorithms, this study reviewed, by following the PRISMA guidelines, existing studies from the literature that used such algorithms for disease prediction. The results of this study will help the scholars to better understand current trends and hotspots of disease prediction models using supervised machine learning algorithms and formulate their research goals accordingly. In addition, the advantages and limitations of different supervised machine learning algorithms are summarised. Therefore, this research aims to identify key trends among different types of supervised machine learning algorithms, their performance accuracies and the types of diseases being studied. Specifically, we found little research that makes a comprehensive review of published articles employing different supervised learning algorithms for disease prediction. Given the growing applicability and effectiveness of supervised machine learning algorithms on predictive disease modelling, the breadth of research still seems progressing. For the test set, patients are classified into several groups such as low risk and high risk. Models based on these algorithms use labelled training data of patients for training. Our research focuses on the disease risk prediction models involving machine learning algorithms (e.g., support vector machine, logistic regression and artificial neural network), specifically - supervised learning algorithms. These electronic data are being utilised in a wide range of healthcare research areas such as the analysis of healthcare utilisation, measuring performance of a hospital care network, exploring patterns and cost of care, developing disease risk prediction model, chronic disease surveillance, and comparing disease prevalence and drug outcomes. This is primarily due to the wide adaptation of computer-based technology into the health sector in different forms (e.g., electronic health records and administrative data) and subsequent availability of large health databases for researchers. Disease prediction and in a broader context, medical informatics, have recently gained significant attention from the data science research community in recent years. The scope of this research is primarily on the performance analysis of disease prediction approaches using different variants of supervised machine learning algorithms. ![]() In the supervised variant, a prediction model is developed by learning a dataset where the label is known and accordingly the outcome of unlabelled examples can be predicted. Most of these applications have been implemented using supervised variants of the machine learning algorithms rather than unsupervised ones. These algorithms have a wide range of applications, including automated text categorisation, network intrusion detection, junk e-mail filtering, detection of credit card fraud, customer purchase behaviour detection, optimising manufacturing process and disease modelling. Machine learning algorithms employ a variety of statistical, probabilistic and optimisation methods to learn from past experience and detect useful patterns from large, unstructured and complex datasets.
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