A Web-Based Clinical Decision Support System for the Management of Diabetes Neuropathy Using Naïve Bayes Algorithm (Published)
Diabetes Neuropathy is a chronic health problem with devastating, yet preventable consequences. Due to this shortage of specialists, there is a need for a Clinical Decision Support System that will diagnose and manage diabetes neuropathy. This work therefore aimed at designing a web-based Clinical Decision Support System for the management of early diabetes neuropathy. Four pattern classification algorithms (K-nearest neighbor, Decision Tree, Decision Stump and Rule Induction) were adopted in this work and were evaluated to determine the most suitable algorithm for the clinical decision support system. Datasets were gathered from reliable sources; two teaching hospitals in Nigeria, these were used for the evaluation Benchmarks such as performance, accuracy level, precision, confusion matrices and the models building’s speed were used in comparing the generated models. The study showed that Naïve Bayes outperformed all other classifiers with accuracy being 60.50%. k-nearest neighbor, Decision Tree, Decision Stump and Rule induction perform well with the lowest accuracy for x- cross validation being 36.50%. Decision Tree falls behind in accuracy, while k-nearest neighbour and Decision Stump maintain accuracy at equilibrium 41.00%. Therefore, Naïve Bayes is adopted as optimal algorithm in the domain of this study. The rules generated from the optimal algorithm (Naïve Bayes) forms the back-end engine of the Clinical Decision Support System. The web-based clinical decision support system was then designed The automatic diagnosis of diabetes neuropathy is an important real-world medical problem. Detection of diabetes neuropathy in its early stages is a key for controlling and managing patients early before the disabling effect present. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. For further studies, researchers can improve on the proposed clinical decision support system by employing more than one efficient algorithm to develop a hybrid system.
Keywords: Accuracy, Algorithm, Classification, Diabetes, Neuropathy, precision
Web Data Mining: Views of Criminal Activities (Published)
Web data mining discovers valuable information or knowledge from the web hyperlink structure, page content and usage data. Along with the swift popularity of the Internet, crime information on the web is becoming increasingly flourishing, and the majority of them are in the form of text. A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting, exploring crimes and investigating their relationship with criminals are a big challenge to the present world. The evaluation of the different dimensions of widespread criminal web data causes one of the research challenges to the researchers. Criminal web data always offer convenient and applicable information for law administration and intelligence department. The goal of crime data mining is to understand patterns in criminal behavior in order to predict crime anticipate criminal activity and stop it. This paper describes web data mining which includes structure mining, web content mining, web usage mining and crime data mining. The occurrences of criminal activities based on web data mining process is also presented in this paper. The presented information on different criminal activities can be used to reduce further occurrences of similar incidence and to stop the crime.
Keywords: Classification, Clustering., Crime Control., Crime data, Pattern Analysis, Web Mining
Dynamic Decision Tree Based Ensembled Learning Model to Forecast Flight Status (Published)
This paper explains the development of an enhanced predictive classifier for flight status that will reduce over fitting observed in existing models. A dynamic approach from ensemble learning technique called bagging algorithm was used to train a number of base learners using a base learning algorithm. The results of the various classifiers were combined, voting was done, by majority the most voted class was picked as the final output. This output was subjected to the decision tree algorithm to produce various replica sets generated from the training set to create various decision tree models. Object-Oriented Analysis and Design (OO-AD) methodology was adopted for the design and implementation was done with C# programming language. The result achieved was favorable as it was found to predict at an accuracy of 78.3% as against 68.2% accuracy of the existing systems which indicated an enhancement.
Keywords: : Flight Status, Bagging Algorithm, Classification, Ensemble learning, Prediction
Web Data Mining: Views of Criminal Activities (Published)
Web data mining discovers valuable information or knowledge from the web hyperlink structure, page content and usage data. Along with the swift popularity of the Internet, crime information on the web is becoming increasingly flourishing, and the majority of them are in the form of text. A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting, exploring crimes and investigating their relationship with criminals are a big challenge to the present world. The evaluation of the different dimensions of widespread criminal web data causes one of the research challenges to the researchers. Criminal web data always offer convenient and applicable information for law administration and intelligence department. The goal of crime data mining is to understand patterns in criminal behavior in order to predict crime anticipate criminal activity and stop it. This paper describes web data mining which includes structure mining, web content mining, web usage mining and crime data mining. The occurrences of criminal activities based on web data mining process is also presented in this paper. The presented information on different criminal activities can be used to reduce further occurrences of similar incidence and to stop the crime.
Keywords: Classification, Clustering., Crime Control., Crime data, Pattern Analysis, Web Mining
Web Page Classification by using CPBF and Neural Network (Review Completed - Accepted)
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With the exclusive growth in the WWW makes the internet growing very fast. Therefore classifiers of the web pages become more challenging. The proposed system is about using Class Profile- Based Features CPBF for features selection. In this research, new web page classification method is proposed, using neural network with inputs obtained by CPBF. The fixed number of regular words from each class will be used as a feature vector, these feature vector are then used as the input to the neural networks for classification. The experimental evaluation demonstrates that the method provides high quality classification accuracy with the sports news datasets
Keywords: CPBF, Classification, WWW, Web- Page Classification