Background: Chronic kidney disease is one of a major global public health issue, affecting over 10% of the population worldwide. It is the leading cause of death in 2016 ranking 16th and is expected to rise to 5th rank by 2040.Consequently, tools to identify patients at high risk of having CKD and management of risk factors are needed, particularly in limited-resources settings where laboratory facilities are scarce. This study aimed to develop a risk prediction and management system using data from JUMC, SPHMMC and MTUTH. Objective: To develop chronic kidney disease risk prediction and management system is using expert system. Method :General chronic kidney disease risk factor were collected from expert knowledge .The identified general risk factors were applied on 384 patients data collected from three hospitals to identify risk factors in Ethiopia .The risk factors were identified using statistical analysis .After identifying the risk factors from the statistical analysis ,risk factor managements techniques were identified from expert knowledge. Knowledge gained from the expert knowledge and statistical analyses were combined and developed using rule based expert system. Main outcome measure: Accuracy, Precision and recall are the parameters which have been evaluated from the developed system using confusion matrix. Result: The system has showed 63.3 %, 65.3 %and 77.5%accuracy at 14%, 24% and 34% cut off percent respectively in estimating probability. Conclusion: This study will have significance in preventing chronic kidney disease at early stage and creating awareness. Funding Statement: The authors received no specific funding for this study.
Background: Clinically Cardiotocography is a technique which is used to monitor and evaluate the level of fetal distress. Even though, CTG is the most widely used device to monitor determine the fetus health, existence of high false positive result from the visual interpretation has a significant contribution to unnecessary surgical delivery or delayed intervention. Objective: In the current study an innovative computer aided fetal distress diagnosing model is developed by using time frequency representation of FHR signal using generalized Morse wavelet and the concept of transfer learning of pre-trained ResNet 50 deep neural network model. Method: The CTG data is obtained from the only open access CTU-UHB data base of Cardiotocogram, and then FHR signal is extracted and preprocessed to remove noises and spikes. After preprocessing the time frequency information of FHR signal is extracted by using generalized Morse wavelet and fed to a pre-trained ResNet 50 model which is fine tuned and configured according to our dataset. Result: After successfully training the model, a comprehensive experimentation of testing is conducted for FHR data for which a recording is made during early stage of labor and last stage of labor. Thus, a promising classification accuracy result of 98.7% and 96.1 are achieved for FHR data’s of 1st and 2nd stage of labor respectively. Conclusion: A graphical user interface is developed for the model using Matlab app designer for ease of implementation, and can be used as a decision-making aid system for obstetrician and gynecologist.
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