Defining the rehabilitation treatment programs for stroke patients by applying Neural Network and Decision Trees models

Abstract

At present, patients whose have suffered from stroke in Thailand are increasing every year. Stroke impairments relate to many functions such as sensory, motor function, communication, visual and emotional function which depend on brain's lesion. Physical examinations and assessments are important for planning the rehabilitation programs. For this reason, there are several information for medical decision making. Missing some data for treatment planning may occur. To solve this problem, the proposed study used two algorithms to determine the proper rehabilitation treatment program. Artificial Neural Networks and Decision Trees models were considered. Sensitivity, specificity and accuracy values were computed to define the performance of both algorithms. The results of this study indicated that both techniques can apply for data classification and define the proper treatment programs. However, the results were shown that the specificity and accuracy of decision trees model were higher than neural network model.

Comparison of Bagging and Adaboost Methods on C4.5 Algorithm for Stroke Prediction

“…Penelitian pertama yaitu penelitian yang berjudul mendefinisikan program perawatan rehabilitasi pasien stroke dengan menerapkan model Neural Network dan Decision Tree. Dalam penelitian tersebut menjelaskan bahwa algoritma Decision Tree dan Neural Network dapat menentukan program perawatan yang tepat dan nilai akurasi yang dihasilkan oleh algoritma Decision Tree lebih unggul dari algoritma Neural Network, Selain itu nilai sensitivitas kedua algoritma tersebut adalah sama, tetapi nilai spesifisitas dan akurasinya tidak sama [18].…”