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This research aimed (1) to create cardiovascular risk diagnosis prediction models using algorithms including Neural Network, Random Forest, Naïve Bayes, K-Nearest Neighbors and Decision Tree (2) five algorithms were used with attribute selection and (3) comparing the model performance using 10f Fold cross validation method. Tools usee were MySQL and RapidMiner Studio programs. The data see comprised people who had been screened as patients with cardiovascular disease that were collected from the Saraburi Provincial Public Health Office during 2018-2019 from 12 Saraburi hospitals and 126 health promoting hospitalsreIt was found that the model with the best prediction performance was the neural network model with attribute selection having 99.29% accuracy, and the lowest was the decision tree model with 70.39% accuracy. This research concluded that the neural network model with attribute selection of the best qualificationt should be further developed for early diagnosis of cardiovascular risk web applications. [ABSTRACT FROM AUTHOR]

This research aims to study the performance of data mining techniques in medical datasets. The data in this research contaita information of patients with breast cancer, diabetics and patients with hyperthyroidism. All datasets were collected from UCI databasee Mmachine learning, in particular Decision Tree C4.5, Naïve Bayes, Neural Networks, Random Forest and Deep Learning techniques were used to create the models of disease Breast cancer, diabetes and hypothyroidism prediction models. 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 experimental results showed that the Decision Tree C4.5 technique was the best technique in modeling the prognosis of hypothyroidism. It provided 99.86 % accuracy, 99.85 % sensitivity and 100 % specificity. [ABSTRACT FROM AUTHOR]