O. O. Oladipupo

Improving medical rule-based expert systems comprehensibility: fuzzy association rule mining approach

In this paper, a Fuzzy Association Rule Mining (FARM) with expert-driven approach is proposed to acquire a knowledge-base, which corresponds more intuitively to human perception with a high comprehensibility. This approach reduces the number of rules in the knowledge-base when compared with the Standard Rule-base Formulation (SRF) and makes possible the rating of the rules according to their relevance. The rule relevance is determined by the measures of significance and certainty factors. The approach is validated using a medical database and the result shows that this approach ultimately reduces the number of rules and enhances the comprehensibility of the expert system.

In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.

Enhancing Business Decision Making Through Actionable Knowledge Discovery Using an Hybridized MCDM Model

In recent years, with the increase in the usage of internet-enabled electronic devices and information systems, the upsurge and availability of volumes of high dimensional data have become one of the sources of high business value. The need for businesses to make informed decisions by leveraging on the patterns from the multi-dimensional data have become paramount. However, the major issue is whether or not the patterns can optimize business decision making process to increase profit. Hence, there is need for actionable knowledge discovery (AKD). Therefore, this paper proposed an hybridized interval type-2 fuzzy Multi Criteria Decision Making (MCDM) model for evaluating patterns based on three subjective interestingness measure which are unexpectedness, actionability and novelty. The interval type-2 Fuzzy Analytical Hierarchy Process (AHP) was employed to weigh the patterns and Compensatory AND approach was utilized for ranking the patterns using the three subjective interestingness measures. The proposed model depicts its applicability in identifying and ranking the patterns which are more relevant for enhancing business decision making.