Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms

Exploring the dataset features through the application of clustering algorithms is a viable means by which the conceptual description of such data can be revealed for better understanding, grouping and decision making. Some clustering algorithms, especially those that are partitioned-based, clusters any data presented to them even if similar features do not present. This study explores the performance accuracies of partitioning-based algorithms and probabilistic model-based algorithm. Experiments were conducted using k-means, k-medoids and EM-algorithm. The study implements each algorithm using RapidMiner Software and the results generated was validated for correctness in accordance to the concept of external criteria method. The clusters formed revealed the capability and drawbacks of each algorithm on the data points

Microarray cancer feature selection: Review, challenges and research directions

Microarray technology has become an emerging trend in the domain of genetic research in which many researchers employ to study and investigate the levels of genes’ expression in a given organism. Microarray experiments have lots of application areas in the health sector such as diseases prediction and diagnosis, cancer study and soon. The enormous quantity of raw gene expression data usually results in analytical and computational complexities which include feature selection and classification of the datasets into the correct class or group. To achieve satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task. Therefore, this paper presents a comprehensive survey of studies on microarray cancer classification with a focus on feature selection methods. In this paper, the taxonomy of the various feature selection methods used for microarray cancer classification and open research issues have been extensively discussed

An Empirical Investigation of the Prevalence of Osteoarthritis in South West Nigeria: A Population-Based Study

Today, Osteoarthritis remains the most prevalent chronic joint disease and a potentially incapacitating joint illness. It is an enduring health problem which cannot be cure though it can be managed. Osteoarthritis remains a serious public health problem because its burden is high, people who live with it have a greater risk of developing anxiety / or depression and if it is not properly managed, it can bring about disability as well as impairing quality of life. This paper presents a statistical correlation between the reported risk factors of Osteoarthritis and its prevalence in Nigeria. Statistical tests were performed to investigate if there is enough evidence for inferring that the risk factors for Osteoarthritis are true for the whole of Nigerian populatio

An Empirical Investigation of the Prevalence of Osteoarthritis in South West Nigeria: A Population-Based Study

Today, Osteoarthritis remains the most prevalent chronic joint disease and a potentially incapacitating joint illness. It is an enduring health problem which cannot be cure though it can be managed. Osteoarthritis remains a serious public health problem because its burden is high, people who live with it have a greater risk of developing anxiety / or depression and if it is not properly managed, it can bring about disability as well as impairing quality of life. This paper presents a statistical correlation between the reported risk factors of Osteoarthritis and its prevalence in Nigeria. Statistical tests were performed to investigate if there is enough evidence for inferring that the risk factors for Osteoarthritis are true for the whole of Nigerian population</jats:p

An Empirical Investigation of the Prevalence of Osteoarthritis in South West Nigeria: A Population-Based Study

Today, Osteoarthritis remains the most prevalent chronic joint disease and a potentially incapacitating joint illness. It is an enduring health problem which cannot be cure though it can be managed. Osteoarthritis remains a serious public health problem because its burden is high, people who live with it have a greater risk of developing anxiety / or depression and if it is not properly managed, it can bring about disability as well as impairing quality of life. This paper presents a statistical correlation between the reported risk factors of Osteoarthritis and its prevalence in Nigeria. Statistical tests were performed to investigate if there is enough evidence for inferring that the risk factors for Osteoarthritis are true for the whole of Nigerian populatio

Nature-Inspired Meta-heuristic Optimization Algorithms for Breast Cancer Diagnostic Model: A Comparative Study

The selection of features is used to obtain a subset of features by the removal of irrelevant features with no or less predictive output. Meta-heuristic algorithms are appropriate for the selection of features because feature subset representation is direct and the evaluation is easily accomplished. This paper performed a comparative study on the impact of meta-heuristic optimization algorithms on breast cancer diagnosis using Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The two feature selection algorithms were used to obtain the relevant attributes from the Wisconsin breast cancer (original) dataset. The selected attributes were passed to seven learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naïve Bayes (NB), K Nearest Neighhood (KNN), Neural Network (NN), Logistic Regression (LR), and Random Forest (RF). The diagnostic model was evaluated based on accuracy, precision, recall, and F1-measure. Experimental showed that the highest accuracy of 97.1388% was obtained in both PSO and ACO using RF classifier, the highest precision value of 0.9720 was recorded in ACO using RF classifier,  the highest recall value of 0.9750 was achieved in PSO using RF classifier, the highest F1-measure value of 0.9700 was obtained in PSO using SVM, the highest kappa statistic of 0.9370 was obtained in both PSO and ACO using RF and the lowest time of 0s was taken to build a model was recorded in PSO using KNN and NB, and also in ACO using KNN. The paper concluded that the breast diagnostic model using PSO and ACO with different learning algorithms revealed that the accuracy of RF outperformed other algorithms. Also, it was shown that ACO produced better precision using RF compared with PSO and PSO gave better recall using RF compared with ACO, PSO recorded an efficient F1-measure using SVM. The best time used to build a model was obtained in PSO for KNN and NB, and ACO with KNN.Keywords— Breast cancer, Data mining, Diagnosis, Feature selection, Meta-heuristic. </jats:p

Performance Evaluation: Dataset on the scholastic performance of students in 12 programmes from a private university in the south-west geopolitical zone in Nigeria

Dataset of educational performances of college students in 12 programmes in a private university in Nigeria. The overall people sampled for the observation is 2490 undergraduates excavated from 12 programmes which are as follows Computer Science (CIS), Mathematics (MAT), Electrical and Electronics Engineering (EEE), Biochemistry (BCH), Mechanical Engineering (MCE), Microbiology (MCB), Civil Engineering (CVE), Computer Engineering (CEN), Chemical Engineering (CHE), Industrial Chemistry (CHM), Information and Communication (ICE), Petroleum Engineering (PET)

Evaluation of the scholastic performance of students in 12 programs from a private university in the south-west geopolitical zone in Nigeria

Cumulative grade point average (CGPA) is a system for calculation of GPA scores and is one way to determine a student's academic performance in a university setting. In Nigeria, an employer evaluates a student's academic performance using their CGPA score. For this study, data were collected from a student database of a private school in the south-west geopolitical zone in Nigeria. Regression analysis, correlation analysis, and analysis of variance (F-test) were employed to determine the study year that students perform better based on CGPA. According to the results, it was observed that students perform much better in year three (300 Level) and year four (400 Level) compared to other levels. In conclusion, we strongly recommend the private university to introduce program that will improve the academic performance of students from year one (100 level).</ns4:p