Chronic kidney disease (CKD) is a health complication faced by almost every nation, as Ghana is no exception. The Ghana Dialysis Service Foundation (DSF) indicated that an average of 12,000 kidney-failure cases is diagnosed among Ghanaians every year. An adequate diagnosis of suspected CKD will play a critical role in saving many lives. This study widens the predictive factors grouped as; personal lifestyle, laboratory findings, and medical history of a patient. The glomerulus filtration rate addresses the controversy of diagnosing CKD without considering aetiology.
The study adopted a structured, guided questionnaire to obtain the dataset from health records of 180 patients diagnosed with CKD. The Renal clinic of Komfo Anokye Teaching hospital gave access to the patients' health records following the clearance from the ethical committee in Kwame Nkrumah University of Science and Technology. The health records were categorized into Personal Lifestyle, Laboratory findings and Medical history. Eleven factors that influence the incidence of chronic kidney disease were identified and analyzed in this work. The developed model is based on a hybrid of Artificial Neural Network and Fuzzy logic (ANFIS) techniques.
An experimental result of the proposed predictive model recorded an average Root Mean Square Error of 0.85014 for training and 1.3983 for testing. The result showed a close relationship between the actual and predicted outcomes for the training and testing. The Modification of Diet for Renal Disease (MDRD) model is applied to the dataset to estimate the Glomerulus Filtration Rate to determine the stage of the Chronic Kidney Disease. The developed model (NANFIS) gave a better performance than the Modification of Diet for Renal Disease MODEL results.
Considering the aetiology that widens the predictive factors provides an improved perspective in predicting the stage of the disease. The Modification of Diet for Renal Disease model considers Age, race, sex and creatinine to determine the location of Chronic Kidney Disease. The increase in data would help to improve the performance of the developed model better.