Using feature selection as accuracy benchmarking in clinical data mining.

Automated prediction of new patients’ disease diagnosis based on data mining analysis on historical data is proven to be an extremely useful tool in the medical innovation. There are several studies focusing on this particular aspect. The objective of this study is two-fold. First, we look into three different classifiers, which are the Naïve Bayes, Multilayer Perceptron (MLP) and Decision Tree J48 to predict the diagnosis results. Next, we investigate the effects of feature selection in such experiments. We also compare the experimental results with the study of Comparative Disease Profile (CDP) using the same dataset. Results have shown that the Naive Bayes provides the best result in terms of accuracy in our experiments and in comparison with CDP. However, we suggest using Multilayer Perceptron since the variables used in our experiments are inter-dependent among each other. In addition, MLP has shown better accuracy than CDP

The Application of K-Means Algorithm for LQ45 Index on Indonesia Stock Exchange

The objective of this study is to apply cluster analysis or also known as clustering on stocks data listed in LQ45 index at Indonesia Stock Exchange. The problem is that traders need a tool to speed up decision-making process in buying, selling and holding their stocks.The method used in this cluster analysis is k-means algorithm. The data used in this study were taken from Indonesia Stock Exchange. Cluster analysis in this study took data\u27s characteristics such as stocks volume and value. Results of cluster analysis were presented in the form of grouping of clusters\u27 members visually. Therefore, this cluster analysis in this study could be used to identify more quickly and efficiently about the members of each cluster of LQ45 index. The results of such identification can be used by beginner-level investors who have started interest in stock investment to help make decision on stocks trading

Application of K-Means Algorithm for Cluster Analysis on Poverty of Provinces in Indonesia

The objective of this study was to apply cluster analysis or also known as clustering on poverty data of provinces all over Indonesia.The problem was that the decision makers such as central government, local government and non-government organizations, which involved in poverty problems, needed a tool to support decision-making process related to social welfare problems. The method used in the cluster analysis was kmeans algorithm. The data used in this study were drawn from Badan Pusat Statistik (BPS) or Central Bureau of Statistics on 2014.Cluster analysis in this study took characteristics of data such as absolute poverty of each province, relative number or percentage of poverty of each province, and the level of depth index poverty of each province in Indonesia. Results of cluster analysis in this study are presented in the form of grouping ofclusters' members visually. Cluster analysis in the study can be used to identify more quickly and efficiently on poverty chart of all provinces all over Indonesia. The results of such identification can be used by policy makers who have interests of eradicating the problems associated with poverty and welfare distribution in Indonesia, ranging from government organizations, non-governmental organizations, and also private organizations

A Novel Clinical Decision Support System Using Improved Adaptive Genetic Algorithm for the Assessment of Fetal Well-Being

A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm

Decision support continuum paradigm for cardiovascular disease: Towards personalized predictive models

Clinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few century ago, the first major transformation of medical practice took place as science-based approaches emerged with compelling results. Now, in the 21st century, new advances in science will once again transform healthcare. Data science has been postulated as an important component in this healthcare reform and has received escalating interests for its potential for ‘personalizing’ medicine. The key advantages of having personalized medicine include, but not limited to, (1) more effective methods for disease prevention, management and treatment, (2) improved accuracy for clinical diagnosis and prognosis, (3) provide patient-oriented personal health plan, and (4) cost containment. In view of the paramount importance of personalized predictive models, this thesis proposes 2 novel learning algorithms (i.e. an immune-inspired algorithm called the Evolutionary Data-Conscious Artificial Immune Recognition System, and a neural-inspired algorithm called the Artificial Neural Cell System for classification) and 3 continuum-based paradigms (i.e. biological, time and age continuum) for enhancing clinical prediction. Cardiovascular disease has been selected as the disease under investigation as it is an epidemic and major health concern in today’s world. We believe that our work has a meaningful and significant impact to the development of future healthcare system and we look forward to the wide adoption of advanced medical technologies by all care centres in the near future.Open Acces

The role of mining in community sustainability in Newfoundland and policy implications: a case study of Baie Verte

The island of Newfoundland is very important to the Canadian mining industry. It houses some of Newfoundland and Labrador’s (NL) major mining operations and exploration activities. Commercial mining, which began in 1864 on the island, has historically impacted the sustainability of communities. Using a mixed methods case study approach, the study first employed a three capitals sustainability assessment model, the ‘Telos framework’, to assess the current state of community sustainability in the Town of Baie Verte. The study then examined the impacts of mining on the assessed state of sustainability in Baie Verte and explored the role of government and corporate policies in these impacts. The study findings indicate that the Town of Baie Verte has had two mining eras (the old era from1960s-1996, and the new era from 2010-present). The mining operations in the old era, on the one hand, made significant contributions to the economic and socio-cultural capital of Baie Verte, but were environmentally destructive with adverse impacts on health and safety. The mining operations in the new or current era, on the other hand, seem to have good environmental performance so far, but have made fewer contributions to the economic and socio-cultural capitals of Baie Verte compared to the previous era. In terms of the role of policy, overall, both government and corporate policies seem to have played more of an environmental role than an economic or socio-cultural role in enhancing the sustainability of mining communities in NL; but in the case of Baie Verte, corporate policies have made significant socio-economic contributions as well. Two recently launched federal and NL provincial government sustainable mining policy initiatives, namely the Canadian Minerals and Metals Plan (CMMP) and Mining the Future 2030, a Plan for Growth in the NL Mining Industry, have the potential to supplement other existing policies and plans and to enhance the overall sustainability of mining communities in NL, including Baie Verte. In order to achieve this, collaboration will be needed between stakeholders in government, industry, and local communities for implementing these plans