Timely Prediction of Diabetes by Means of Machine Learning Practices

• Published in: Augmented human research Vol. 8; no. 1
• Main Authors: Tripathi, Rajan Prasad, Sharma, Manvinder, Gupta, Anuj Kumar, Pandey, Digvijay, Pandey, Binay Kumar, Shahul, Aakifa, George, A. S. Hovan
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01-12-2023
• Subjects: Biomedical Engineering and Bioengineering; Cognitive Psychology; Computational Intelligence; Engineering; Human Physiology; Original Paper; Robotics and Automation; User Interfaces and Human Computer Interaction; Data science; Diabetes; Soft voting; Machine learning; Ensemble
• ISSN: 2365-4317; 2365-4325
• DOI: 10.1007/s41133-023-00062-4

The quality and quantity of medical data produced by digital devices have improved significantly in recent decades. This has led to cheap and easy data generation. There has therefore been an increased advantage in the areas of Big Data and machine learning. There is a huge application of machine leaning and artificial intelligence in health care sector. The use of machine learning to train the machine to classify the medical cases taking care of the historical data can be a boon in medical studies. In this paper, we have analyzed many machine learning algorithms and classifiers which are used to make prediction on the diabetes based on the chosen features and attributes of the dataset. The implementation of the algorithms and its performance are compared in terms of accuracy; we have also used the soft voting ensemble techniques and applied the standardized PIMA diabetes data for which the highest accuracy is achieved.