Monitoring of Disable Patients with their Medical conditions using K-NN Classifier Algorithm

Abstract

In this paper a general procedure for the infirmity assessment of patients with ineptitudes is presented. It relies upon consistently activity examination using ordinary accelerometer sensors conveyed by the noticed person while at home without clinical staff present. The k-NN classifier is being used to study six different types of activities after preliminary data pre-planning, followed by further decreases in estimation and confirmation time. Promising results are gotten which could bring supportive bits of knowledge for the progression of the clinical state of the saw individual over the course of postponed time. The simulation outcome shows that feature vectors over the central components say x,y, and z and separation the parts by the algorithm employed gives accurate classification with the applied features. Since each development has otherworldly show over specific course which gives non-uniform scattering for the base organize hatchets two or three fitting parts may be used. And also it is easier to see the patients data or patients record can be classified for getting different features accurately with minimal time and the same data used for estimation of his/her features quickly. Thus employing K-NN algorithm classification and features set can be extracted and medical conditions of the patients know in faster time.