Knowledge-based Systems and Interestingness Measures: Analysis with Clinical Datasets

Knowledge mined from clinical data can be used for medical diagnosis and prognosis. By improving the quality of knowledge base, the efficiency of prediction of a knowledge-based system can be enhanced. Designing accurate and precise clinical decision support systems, which use the mined knowledge, is still a broad area of research. This work analyses the variation in classification accuracy for such knowledge-based systems using different rule lists. The purpose of this work is not to improve the prediction accuracy of a decision support system, but analyze the factors that influence the efficiency and design of the knowledge base in a rule-based decision support system. Three benchmark medical datasets are used. Rules are extracted using a supervised machine learning algorithm (PART). Each rule in the ruleset is validated using nine frequently used rule interestingness measures. After calculating the measure values, the rule lists are used for performance evaluation. Experimental results show variation in classification accuracy for different rule lists. Confidence and Laplace measures yield relatively superior accuracy: 81.188% for heart disease dataset and 78.255% for diabetes dataset. The accuracy of the knowledge-based prediction system is predominantly dependent on the organization of the ruleset. Rule length needs to be considered when deciding the rule ordering. Subset of a rule, or combination of rule elements, may form new rules and sometimes be a member of the rule list. Redundant rules should be eliminated. Prior knowledge about the domain will enable knowledge engineers to design a better knowledge base

Computer-assisted Medical Decision-making System for Diagnosis of Urticaria

Background: Urticaria is a common allergic disease that affects all age groups. Allergic disorders are diagnosed at allergy testing centers using skin tests. Though skin tests are the gold standard tests for allergy diagnosis, specialists are required to interpret the observations and test results. Hence, a computer-assisted medical decision-making (CMD) system can be used as an aid for decision support, by junior clinicians, in order to diagnose the presence of urticaria. Methods: The data from intradermal skin test results of 778 patients, who exhibited allergic symptoms, are considered for this study. Based on food habits and the history of a patient, 40 relevant allergens are tested. Allergen extracts are used for skin test. Ten independent runs of 10-fold cross-validation are used to train the system. The performance of the CMD system is evaluated using a set of test samples. The test samples were also presented to the junior clinicians at the allergy testing center to diagnose the presence or absence of urticaria. Results: From a set of 91 features, a subset of 41 relevant features is chosen based on the relevance score of the feature selection algorithm. The Bayes classification approach achieves a classification accuracy of 96.92% over the test samples. The junior clinicians were able to classify the test samples with an average accuracy of 75.68%. Conclusion: A probabilistic classification approach is used for identifying the presence or absence of urticaria based on intradermal skin test results. In the absence of an allergy specialist, the CDM system assists junior clinicians in clinical decision making

Multimodal Characterization of the Morphology and Functional Interfaces in Composite Electrodes for Li–S Batteries by Li Ion and Electron Beams

We report the characterization of multiscale 3D structural architectures of novel poly­[sulfur-<i>random</i>-(1,3-diisopropenylbenzene)] copolymer-based cathodes for high-energy-density Li–S batteries capable of realizing discharge capacities >1000 mAh/g and long cycling lifetimes >500 cycles. Hierarchical morphologies and interfacial structures have been investigated by a combination of focused Li ion beam (LiFIB) and analytical electron microscopy in relation to the electrochemical performance and physicomechanical stability of the cathodes. Charge-free surface topography and composition-sensitive imaging of the electrodes was performed using recently introduced low-energy scanning LiFIB with Li⁺ probe sizes of a few tens of nanometers at 5 keV energy and 1 pA probe current. Furthermore, we demonstrate that LiFIB has the ability to inject a certain number of Li cations into the material with nanoscale precision, potentially enabling control of the state of discharge in the selected area. We show that chemical modification of the cathodes by replacing the elemental sulfur with organosulfur copolymers significantly improves its structural integrity and compositional homogeneity down to the sub-5-nm length scale, resulting in the creation of (a) robust functional interfaces and percolated conductive pathways involving graphitic-like outer shells of aggregated nanocarbons and (b) extended micro- and mesoscale porosities required for effective ion transport