Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Optimised dose titration for Duodopatreatment based on simulation experiments– implementation in a decision supportsystem

The aim of this work was to design a set of rules for levodopa infusion dose adjustment in Parkinson’s disease based on a simulation experiments. Using this simulator, optimal infusions dose in different conditions were calculated. There are seven conditions (-3 to +3)appearing in a rating scale for Parkinson’s disease patients. By finding mean of the differences between conditions and optimal dose, two sets of rules were designed. The set of rules was optimized by several testing. Usefulness for optimizing the titration procedure of new infusion patients based on rule-based reasoning was investigated. Results show that both of the number of the steps and the errors for finding optimal dose was shorten by new rules. At last, the dose predicted with new rules well on each single occasion of majority of patients in simulation experiments.

A framework for ICT-based knowledge sharing in sustainable rural development : the case of Gilgit-Baltistan, Pakistan

Some 50% of the people in the world live in rural areas, often under harsh conditions and in poverty. The need for knowledge of how to improve living conditions is well documented. In response to this need, new knowledge of how to improve living conditions in rural areas and elsewhere is continuously being developed by researchers and practitioners around the world. People in rural areas, in particular, would certainly benefit from being able to share relevant knowledge with each other, as well as with stakeholders (e.g. researchers) and other organizations (e.g. NGOs). Central to knowledge management is the idea of knowledge sharing. This study is based on the assumption that knowledge management can support sustainable development in rural and remote regions. It aims to present a framework for knowledge management in sustainable rural development, and an inventory of existing frameworks for that. The study is interpretive, with interviews as the primary source for the inventory of stakeholders, knowledge categories and Information and Communications Technology (ICT) infrastructure. For the inventory of frameworks, a literature study was carried out. The result is a categorization of the stakeholders who act as producers and beneficiaries of explicit and indigenous development knowledge. Stakeholders are local government, local population, academia, NGOs, civil society and donor agencies. Furthermore, the study presents a categorization of the development knowledge produced by the stakeholders together with specifications for the existing ICT infrastructure. Rural development categories found are research, funding, agriculture, ICT, gender, institutional development, local infrastructure development, and marketing & enterprise. Finally, a compiled framework is presented, and it is based on ten existing frameworks for rural development that were found in the literature study, and the empirical findings of the Gilgit-Baltistan case. Our proposed framework is divided in four levels where level one consists of the identified stakeholders, level two consists of rural development categories, level three of the knowledge management system and level four of sustainable rural development based on the levels below. In the proposed framework we claim that the sustainability of rural development can be achieved through a knowledge society in which knowledge of the rural development process is shared among all relevant stakeholders.