• Güler Kalem Department of Software Engineering, Atılım University, Turkey
  • Çiğdem Turhan Department of Software Engineering, Atılım University, Turkey
Keywords: Classification, activity recognition, KNN (K-Nearest Neighbors), LDA (Linear Discriminant Analysis), SVM (Support Vector Machines), healthcare applications, feature extraction, validation techniques.


Mobile applications that are used in the healthcare domain have become more popular in recent years because of their functionality. Additionally, they are easy to access and cheap when compared to applications which are not compatible with mobile devices. Patients who need to lose weight or exercise regularly are willing to use such mobile applications to recognize and track their daily physical activities in an easier and more accurate way. In this study, some of the most popular classification algorithms such as KNN (K-Nearest Neighbors), LDA (Linear Discriminant Analysis) and SVM (Support Vector Machines) are selected and applied to the data set to compare their performances for the recognition of the physical activities; namely, walking, walking quickly and running.


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How to Cite
Kalem, G., & Turhan, Çiğdem. (2018). COMPARISON OF CLASSIFICATION ALGORITHMS: A CASE STUDY FOR PHYSICAL ACTIVITY RECOGNITION. International Journal of Scientific Research in Information Systems and Engineering (IJSRISE), 4(1), 53-56. Retrieved from
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