000			03093nam a22001457a 4500
008			210802b ||||| |||| 00| 0 eng d
100			_aABOLAJI, Jacob Olamide _98469
245			_aDevelopment of a predictive model for the risk of typhoid using data mining techniques
250			_aDr. I. O. Akinyemi
260			_aIbafo _bComputer Science and Mathematics _c2019
300			_axi,; 74 _bdia, table
520			_aTyphoid fever, also called enteric fever, is caused by Salmonella enterica serovarTyphi, a gram-negative bacterium. Estimates for 2000 suggest that around 21.5 million infections and 200 thousand fatalities due to typhoid fever are reported worldwide every year. Typhoid fever and paratyphoid fever, especially Among Nigerian kids and adolescents, keep being significant causes of disease and death. The aim of this study is to use Techniques for Data Mining to develop a Typhoid risk predictive model in Nigerians using relevant risk factors. Historical data on the distribution of typhoid risk among participants were gathered using questionnaires after medical professionals identified linked typhoid risk variables. The predictive model for typhoid risk was developed using the algorithm for decision trees to define and account for variables linked to typhoid risk The Waikato Environment for Knowledge Analysis (WEKA) software – a suite of machine learning algorithms was used to develop the predictive model as the simulation environment. Holdout and 10-fold cross-validation techniques were used to evaluate the performance of the algorithms. The data sets were therefore subject to 10-fold cross validation using the two (2) chosen decision trees learning algorithms, namely: C4.5 implemented as the WEKA J48 algorithm and the Naïve Bayes algorithm. The result of the performance evaluation of the C4.5 and naïve Bayes’ algorithms are presented in Table 4.3. The true positive rate which gave a description of the proportion of actual cases that was correctly predicted which showed values of 0.783, 0.519, 0.722 and 0.619 respectively for no, low, moderate and high risk cases by the C4.5 decision trees algorithm and 0.739, 0.556, 0.611 and 0.667 for the naïve Bayes classifier. The study presented a predictive model of typhoid risk using relevant risk factors selected from a predefined set of typhoid risk factors in Nigerians using the C4.5 decision trees algorithm that outperformed the performance of the classification of the naïve Bayes. A stronger understanding of the connection between the characteristics appropriate to typhoid risk was suggested following the creation of the forecast model for typhoid risk classification. The model can also be incorporated into the current Health Information System (HIS) that captures and manages clinical data that can be supplied to the predictive model of typhoid risk classification, thus enhancing clinical choices influencing typhoid risk and evaluating clinical data that affects typhoid risk from distant places in real time.
650			_aComputer Science _91105
942			_cTHS
999			_c5942 _d5942