<span>The imbalanced data problems in data mining are common nowadays, which occur due to skewed nature of data. These problems impact the classification process negatively in machine learning process. In such problems, classes have different ratios of specimens in which a large number of specimens belong to one class and the other class has fewer specimens that is usually an essential class, but unfortunately misclassified by many classifiers. So far, significant research is performed to address the imbalanced data problems by implementing different techniques and approaches. In this research, a comprehensive survey is performed to identify the challenges of handling imbalanced class problems during classification process using machine learning algorithms. We discuss the issues of classifiers which endorse bias for majority class and ignore the minority class. Furthermore, the viable solutions and potential future directions are provided to handle the problems<em>.</em></span>

Chronic Kidney Disease (CKD) is one of the leading cause of death contributed by other illnesses such as diabetes, hypertension, lupus, anemia or weak bones that lead to bone fractures. Early prediction of CKD is important in order to contain the disesase. However, instead of predicting the severity of CKD, the objective of this paper is to predict the diagnosis of CKD based on the symptoms or attributes observed in a particular case, whether the stage is acute or chronic. To achieve this, a classification model is proposed to label stage of severity for kidney diseases patients. The experiments then investigated the performance of the proposed classification model based on eight supervised classification algorithms, which are ZeroR, Rule Induction, Support Vector Machine, Naïve Bayes, Decision Tree, Decision Stump, k-Nearest Neighbour, and Classification via Regression. The performance of the all classifiers is evaluated based on accuracy, precision, and recall. The results showed that the regression classifier perform best in the kidney diagnostic procedure.

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Information Management and PSM Evaluation System is a system developed to replace the existing system at the Faculty of Computing. The existing system at the Faculty of Computing is a manual system in which all the evaluation process still utilises paper forms. PSM is divided into two phases; PSM1 and PSM2 and each phase has a different form for evaluation. This process is seen to be less systematic and imposes much time on the evaluator, coordinator and supervisor who are also lecturers. Information Management and PSM Evaluation System is designed to automate information management and evaluation of PSM to keep the information in the database. The scope of these systems focuses on admin, supervisor, evaluator and coordinator bound to PSM1 and PSM2. Some of the functions that can be operated on the system are evaluation, updating PSM students' information and generating reports. The chosen methodology is an Evolutionary Prototype which needs are taken care of the system during modifications. Requirements established during the interview is employed to form a common structure with the essential basic functions of the system. Therefore, Information Management and PSM Evaluation System was developed to automate the manual system to increase efficiency. The system was developed using ASP.net technology and Microsoft Visual Studio 2010 and has been successfully completed within the specified time.