Framework Two-Tier Feature Selection on the Intelligence System Model for Detecting Coronary Heart Disease

Coronary heart disease is a non-communicable disease with high mortality. A good action to anticipate this is to do prevention, namely by carrying out a healthy lifestyle and routine early examinations. Early detection of coronary heart disease requires a number of examinations, such as demographics, ECG, laboratory, symptoms, and even angiography. The number of inspection parameters in the context of early detection will have an impact on the time and costs that must be incurred. Selection of the right and important inspection parameters will save time and costs. This study proposes an intelligence system model for the detection of coronary heart disease by using a minimal examination attribute, with performance in the good category. This research method is divided into a number of stages, namely data normalization, feature selection, classification, and performance analysis. Feature selection uses a Two-tier feature selection framework consisting of correlation-based filters and wrappers. The system model is tested using a number of datasets, and classification algorithms. The test results show that the proposed two-tier feature selection framework is able to reduce the highest attribute of 73.51% in the z-Alizadeh Sani dataset. The performance of the system using the bagging-PART algorithm is able to provide the best performance with parameters area under the curve (AUC) 95.4%, sensitivity 95.9% while accuracy is 94.1%. Referring to the AUC value, the proposed system model is included in the good category.

Can We Avoid Tacit Trade-Offs between Flexibility and Efficiency in Systematic Conservation Planning? The Mediterranean Sea as a Case Study

Species distribution models (SDMs) provide robust inferences about species-specific site suitability and are increasingly used in systematic conservation planning (SCP). SDMs are subjected to intrinsic uncertainties, and conservation studies have generally overlooked these. The integration of SDM uncertainties in conservation solutions requires the development of a suitable optimization algorithm. Exact optimization algorithms grant efficiency to conservation solutions, but most of their implementations generate a single binary and indivisible solution. Therefore, without variation in their parameterization, they provide low flexibility in the implementation of conservation solutions by stakeholders. Contrarily, heuristic algorithms provide such flexibility, by generating large amounts of sub-optimal solutions. As a consequence, efficiency and flexibility are implicitly linked in conservation applications: mathematically efficient solutions provide less flexibility, and the flexible solutions provided by heuristics are sub-optimal. To avoid this trade-off between flexibility and efficiency in SCP, we propose a reserve-selection framework, based on exact optimization combined with a post-selection of SDM outputs. This reserve-selection framework provides flexibility and addresses the efficiency and representativeness of conservation solutions. To exemplify the approach, we analyzed an experimental design, crossing pre- and post-selection of SDM outputs versus heuristics and exact mathematical optimizations. We used the Mediterranean Sea as a biogeographical template for our analyses, integrating the outputs of eight SDM techniques for 438 fish species.

Multiscale selection in spatially structured populations

The spatial structure of natural populations is key to many of their evolutionary processes. Formal theories analysing the interplay between natural selection and spatial structure have mostly focused on populations divided into distinct, non-overlapping groups. Most populations, however, are not structured in this way, but rather (self-)organise into dynamic patterns unfolding at various spatial scales. Here, we present a mathematical framework that quantifies how patterns and processes at different spatial scales contribute to natural selection in such populations. To that end, we define the Local Selection Differential (LSD): a measure of the selection acting on a trait within a given local environment. Based on the LSD, natural selection in a population can be decomposed into two parts: the contribution of local selection, acting within local environments, and the contribution of interlocal selection, acting among them. Varying the size of the local environments subsequently allows one to measure the contribution of each length scale. To illustrate the use of this new multiscale selection framework, we apply it to two simulation models of the evolution of traits known to be affected by spatial population structure: altruism and pathogen transmissibility. In both models, the spatial decomposition of selection reveals that local and interlocal selection can have opposite signs, thus providing a mathematically rigorous underpinning to intuitive explanations of how processes at different spatial scales may compete. It furthermore identifies which length scales - and hence which patterns - are relevant for natural selection. The multiscale selection framework can thus be used to address complex questions on evolution in spatially structured populations.