An Improved Model for Clinical Decision Support System

Odikwa Henry, Ugwu Chidiebere, Inyiama Hycinth
2017 International Journal of Artificial Intelligence & Applications  
Misguided information in health care has caused much havoc that have led to the death of millions of people as a result of misclassification, and inconsistent health care records; hence the objective of this paper is to develop an improved clinical decision support system. This system incorporated hybrid system of non-knowledge based and knowledge based decision support system for the diagnosis of diseases and proper health care delivery records using prostate cancer and diabetes datasets to
more » ... etes datasets to train and validate the model. The min-max method was adopted in normalizing the datasets, while genetic algorithm was deployed in initiating the training weights of the MLP. The result obtained in this paper yielded a classification accuracy of 98%, sensitivity of 0.98 and specificity of 100 for prostate cancer and accuracy of 94%, sensitivity of 0.94 and specificity of 0.67 for diabetes.
doi:10.5121/ijaia.2017.8604 fatcat:66rxcfdldnctbehqpbk5inaf7m