Chronic kidney disease (CKD) is among the top 20 causes of death worldwide and affects approximately 10% of the world adult population. CKD is a disorder that disrupts normal kidney function. Due to the increasing number of people with CKD, effective prediction measures for the early diagnosis of CKD are required. The novelty of this study lies in developing the diagnosis system to detect chronic kidney diseases. This study assists experts in exploring preventive measures for CKD through early diagnosis using machine learning techniques. This study focused on evaluating a dataset collected from 400 patients containing 24 features. The mean and mode statistical analysis methods were used to replace the missing numerical and the nominal values. To choose the most important features, Recursive Feature Elimination (RFE) was applied. Four classification algorithms applied in this study were support vector machine (SVM), k-nearest neighbors (KNN), decision tree, and random forest. All the classification algorithms achieved promising performance. The random forest algorithm outperformed all other applied algorithms, reaching an accuracy, precision, recall, and F1-score of 100% for all measures. CKD is a serious life-threatening disease, with high rates of morbidity and mortality. Therefore, artificial intelligence techniques are of great importance in the early detection of CKD. These techniques are supportive of experts and doctors in early diagnosis to avoid developing kidney failure.

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Abstract-The aim of this paper is to describe a new Navigation technique to assist the blind and people with low vision to reach their destinations in an indoor environment. The proposed technique is based on our previous positioning technique using active Radio-frequency identification (RFID) technology. A Global Positioning System (GPS) does not work efficiently in indoor environments. This research produced a navigation service for sighted and blind people to assist them to reach their destination of interest via the shortest path. Tags were distributed at known locations and the mobile reader was carried by a user. The system role is to determine the shortest path to the destination which was indicated by a user. When the shortest path is calculated, the system indicates way points in the path which were represented by active tags in this scenario. Then the users are navigated to these points one by one via QR-Code till they reach their destinations. It was implemented in an indoor environment as a simulation system to navigate the user to particular offices with a successful shortest path identification rate and satisfactory navigation results were achieved.

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