Recognition of Idleness among Patients based on Activity using Random Forest over Decision Tree Algorithm.
DOI:
https://doi.org/10.47750/pnr.2022.13.S04.217Keywords:
Decision Tree algorithm, Random forest algorithm, machine learning, Prediction, accuracy, human activity.Abstract
Aim:Prediction of human activity data using Random forest algorithm against Decision tree algorithm for better accuracy
using machine learning. Materials and Methods: Random forest algorithm (N=20)and Decision Tree algorithm (N=20) these
two algorithms are calculated by using 2 Groups and I have taken 20 samples for both algorithm and accuracy in this work.
Results: Based on the Results Accuracy obtained in terms of accuracy is identified by Random forest algorithm (90.6%) over
Decision Tree algorithm algorithm(86.66%).Statistical significance difference between Random forest algorithm and Decision
Tree algorithm was found to be 0.001 (p<0.05), using α=0.05 and power=0.80.conclusion: From the analysis it was observed
that the human activity data using Random forest algorithm is better than Decision Tree algorithm for better accuracy using
machine learning.
