Using Data Mining to Predict Possible Future Depression Cases

Depression is a disorder characterized by misery and gloominess felt over a period of time. Some symptoms of depression overlap with somatic illnesses implying considerable difficulty in diagnosing it. This paper contributes to its diagnosis through the application of data mining, namely classification, to predict patients who will most likely develop depression or are currently suffering from depression. Synthetic data is used for this study. To acquire the results, the popular suite of machine learning software, WEKA, is used

Overview applications of data mining in health care: The case study of Arusha region

A research article was submitted to International Journal of Computational Engineering Research||Vol, 03||Issue, 8| 2013Data mining as one of many constituents of health care has been used intensively and extensively in many organizations around the globe as an efficient technique of finding correlations or patterns among dozens of fields in large relational databases to results into more useful health information. In healthcare, data mining is becoming increasingly popular and essential. Data mining applications can greatly benefits all parties involved in health care industry. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. This paper explores data mining applications in healthcare in Arusha region of Tanzania more particularly; it discusses data mining and its applications in major areas such as evaluation of treatment effectiveness, management of healthcare itself and lowering medical cost

Advances in clustering based on inter-cluster mapping

Data mining involves searching for certain patterns and facts about the structure of data within large complex datasets. Data mining can reveal valuable and interesting relationships which can improve the operations of business, health and many other disciplines. Extraction of hidden patterns and strategic knowledge from large datasets which are stored electronically, is therefore a challenge faced by many organizations. One commonly used technique in data mining for producing useful results is cluster analysis. A basic issue in cluster analysis is deciding the optimal number of clusters for a dataset. A solution to this issue is not straightforward as this form of clustering is unsupervised learning and no clear definition of cluster quality exists. In addition, this issue will be more challenging and complicated for multi-dimensional datasets. Finding the estimated number of clusters and their quality is generally based on so-called validation indexes. A limitation with typical existing validation indexes is that they only work well with specific types of datasets compatible with their design assumptions. Also their results may be inconsistent and an algorithm may need to be run multiple times to find a best estimate of the number of clusters. Furthermore, these existing approaches may not be effective for complex problems in large datasets with varied structure. To help overcome these deficiencies, an efficient and effective approach for stable estimation of the number of clusters is essential. Many clustering techniques including partitioning, hierarchal, grid-base and model-based clustering are available. Here we consider only the partitioning method e.g. the k-means clustering algorithm for analysing data. This thesis will describe a new approach for stable estimation of the number of clusters, based on use of the k-means clustering algorithm. First results obtained from the k-means clustering algorithm will be used to gain a forward and backward mapping of common elements for adjacent and non-adjacent clusters. These will be represented in the form of proportion matrices which will be used to compute combined mapped information using a matrix inner product similarity measure. This will provide indicators for the similarity of mapped elements and overlap (dissimilarity), average similarity and average overlap (average dissimilarity) between clusters. Finally, the estimated number of clusters will be decided using the maximum average similarity, minimum average overlap and coefficient of variation measure. The new approach provides more information than an application of typical existing validation indexes. For example, the new approach offers not only the estimated number of clusters but also gives an indication of fully or partially separated clusters and defines a set of stable clusters for the estimated number of clusters. The advantage of the new approach over several existing validation indexes for evaluating clustering results is demonstrated empirically by applying it on a variety of simulated and real datasets

The drivers of Corporate Social Responsibility in the supply chain. A case study.

Purpose: The paper studies the way in which a SME integrates CSR into its corporate strategy, the practices it puts in place and how its CSR strategies reflect on its suppliers and customers relations. Methodology/Research limitations: A qualitative case study methodology is used. The use of a single case study limits the generalizing capacity of these findings. Findings: The entrepreneur’s ethical beliefs and value system play a fundamental role in shaping sustainable corporate strategy. Furthermore, the type of competitive strategy selected based on innovation, quality and responsibility clearly emerges both in terms of well defined management procedures and supply chain relations as a whole aimed at involving partners in the process of sustainable innovation. Originality/value: The paper presents a SME that has devised an original innovative business model. The study pivots on the issues of innovation and eco-sustainability in a context of drivers for CRS and business ethics. These values are considered fundamental at International level; the United Nations has declared 2011 the “International Year of Forestry”