@article{MAKHILLJEAS2019141518161,
    title = {Performance Evaluation of Diagnosis Chronic Kidney Disease using
Support Vector Machine and Logistic Regression Model},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {15},
    pages = {5167-5175},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.5167.5175},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.5167.5175},
    author = {Rizgar and},
    keywords = {Classification,logistic regression,support vector machine,chronic kidney disease,accuracy,kappa coefficient area under curve (ROC)},
    abstract = {With the rapid development of intelligent classification techniques which depends on machine
learning, this study addressed the comparison between one of the traditional statistical models (logistic
regression) with the supervised machine learning model (support vector machine) in order to classify chronic
kidney disease patients based on a blood test (serum) for a group of presence and absence patients. The
dataset contains data of 153 cases and 11 attributes for diagnosis of chronic kidney disease. The dataset were
divided into two groups (training and testing) and after applied the above models depend on evaluation
performance criteria (model accuracy, model sensitivity, model specificity, prevalence, kappa coefficient and
area under curve (ROC)). The study concluded the results indicate SVM Model is the best performer (best
classifier). As well the study concluded through the final fitted models used that the most important factors that
have a clear impact on chronic kidney disease patients are creatinine and urea.}
    }