Model for Predicting the Risk of Kidney Stone using Data Mining Techniques

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dc.contributor.authorOladeji, F.A.
dc.contributor.authorIdowu, P.A.
dc.contributor.authorEgejuru, N.
dc.contributor.authorFaluyi, S.G.
dc.contributor.authorBalogun, J.A.
dc.date.accessioned2019-09-27T07:30:29Z
dc.date.available2019-09-27T07:30:29Z
dc.date.issued2019-01
dc.description.abstractThis paper focused on the development of a predictive model for the classification of the risk of kidney stones in Nigerian using data mining techniques based on historical information elicited about the risk of kidney stones among Nigerians. Following the identification of the risk factors of kidney stone from experienced endocrinologists, structured questionnaires were used to collect information about the risk factors and the associated risk of kidney stones from selected respondents. 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 100%. 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.en_US
dc.identifier.citationOladeji, F.A., Idowu, P.A., Egejuru, N., Faluyi, S.G. & Balogun, J.A. (2019). Model for Predicting the Risk of Kidney Stone using Data Mining Techniques. International Journal of Computer Applications (0975 – 8887). 182(38): 36-56en_US
dc.identifier.issn0975–8887
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/6179
dc.language.isoenen_US
dc.publisherInternational Journal of Computer Applicationsen_US
dc.relation.ispartofseries182;38
dc.subjectKidney Stone Risk Factorsen_US
dc.subjectC4.5en_US
dc.subjectPredictionen_US
dc.subjectClassificationen_US
dc.subjectDecision Treesen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectMultilayer Perceptionen_US
dc.titleModel for Predicting the Risk of Kidney Stone using Data Mining Techniquesen_US
dc.typeArticleen_US
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