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The predictive model for the risk of kidney diseases was formulated using three (3) supervised machine learning algorithms (Decision Tree, Multi-layer perception and Genetic Algorithm) following the identification of relevant features. The predictive model was simulated using the Waikato Environment for Knowledge Analysis (WEKA) environment; and the model was validated using historical dataset of kidney stone risk via performance metrics: accuracy, true positive rate, precision and false positive rate.
The paper concluded that the multi-layer perceptron had the best performance overall using the 33 initially identified variables by the endocrinologists with an accuracy of 100percent. The performance of the genetic programming and multi-layer perceptron algorithms used to formulate the predictive model for the risk of kidney stones using the 6 variables outperformed the model formulated using the 6 variables identified by the C4.5 decision trees. The variables identified by the C4.5 decision trees algorithm were: obese from childhood, eating late at night, BMI class, family history of hypertension, taking coffee and sweating daily. In conclusion, the multi-layer perceptron algorithm is best suitable for the development of a predictive model for the risk of kidney stones.",
Genetic Programming entries for Florence Alaba Oladeji P A Idowu N Egejuru S G Faluyi J A Balogun