S. Anupama Kumar

Mining of student academic evaluation records in higher education

The various data mining techniques like classification, clustering and relationship mining can be applied on educational data to predict the performance of a student in the examination and bring out betterment in his academic performance. Rule based classification techniques can be used to predict the result of the students in the final semester based on the marks obtained by them in the previous semesters. Decision table and One R rule algorithms are used here to predict the result of the fifth semester student based on the marks obtained by the students in the previous four semesters. The accuracy of the algorithms is analyzed by comparing the prediction given by miner, the algorithm and the result obtained by the students in the fifth semester examination. The performances of the algorithms are analyzed using the true positive, false positive values and the ROC curves.

Efficacy of Knowledge Mining and Machine Learning Techniques in Healthcare Industry

Knowledge mining is the process of discovering the knowledge from the larger database. As the size of the data is increasing enormously in the healthcare industry knowledge mining techniques are used to extract and mine the dataset to acquire new knowledge. Machine learning is a technique of training the system. In connection with artificial intelligence, statistics, and computer science, it is also known as statistical learning or predictive analytics. In recent years, application of machine learning and knowledge mining methods is been used everywhere in daily life. Healthcare system can cater prime diagnosis data of human healthcare details and reference to the doctors. Historical medical records afford other healthcare providers to access quickly and recognize the patients past and current health status. Chronic obstructive pulmonary disease is becoming one of the causes for leading deaths. An experiment is conducted to predict the presence and severity of the chronic obstructive pulmonary diseases (COPDs) using knowledge mining and machine learning techniques. Logistic and multinominal regression has been implemented to predict the prevalence of the disease using attributes from various sources and structures.

Efficiency of Multi-instance Learning in Educational Data Mining

Educational data mining (EDM) is one of the emerging technologies in recent years. The various changes in the process of teaching and learning have brought in a lot of challenges to the stakeholders to understand the learners toward the different methods of teaching and the way they perform in various teaching environments. This chapter is an application of Baker’s taxonomy in an educational dataset to predict course outcome of the learners during the middle of the course. The experiment is conducted using different single and multi-instance-based learning algorithms. The efficiency of the single and multi-instance learning algorithms was measured using the accuracy rates and the time taken to build the model. In single instance algorithm, decision stump tree was found very effective and in multi-instance learning, the Simple MI method was found very effective. The precision of the instance-based learning algorithms is calculated using Wilcoxon rank method, and multi-instance learning algorithm is found to be more accurate than the single instance learning techniques.