The performance comparison of data mining techniques for patient incidence
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Abstract
This research aim to study the efficacy of data mining techniques in a wide variety of data. The data in this research contains data of patients with breast cancer. Diabetics And patients with hyperthyroidism. All data were collected from 3 UCI databases. This research, five data mining techniques were used including. Decision Tree C4.5, Naive Bayes, Neural Networks, Random Forest, Deep Learning techniques to create prediction models of simulated for prognosis of disease Breast cancer, diabetes and hypothyroidism. In order to measure the performance of prediction models, 10-fold cross validation was utilized to divide the data into training and testing sets. Accuracy, sensitivity and specificity of the prediction models. were used to compare the prediction performance of each model. The extremal results showed that the Decision Tree technique was the best technique in modeling the prognosis of hypothyroidism. It provides 99.86 % accuracy, 99.85 % sensitivity and 100 % specificity.
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