Measurement of body temperature and heart rate for the development of healthcare system using IOT platform

Health can be define as a state of complete mental, physical and social well-being and not merely the absence of disease or infirmity according to the World Health Organization (WHO) [1]. Having a healthy body is the greatest blessing of life, hence healthcare is required to maintain or improve the health since the healthcare is the maintenance or improvement of health through the diagnosis, prevention, and treatment of injury, disease, illness, and other mental and physical impairments in human beings. The novel paradigm of Internet of Things (IoT) has the potential to transform modern healthcare and improve the well-being of entire society [2]. IoT is a concept aims to connec

Модели согласованного комплексного оценивания в задачах принятия решений

Introduction. The construction of a high-quality comprehensive assessment of an object requires a more accurate and comprehensive accounting of both objective information about the object, subjective assessments, individual experience, intuition and knowledge of the head of the object. Aim. The aim of the work is to develop a modern approach to solving the problems of multi-criteria assessment and ranking based on the formation of a coherent structure of an integrated assessment. To do this, a choice is made of the structure of the complex assessment system (a dichotomous tree, the hanging vertices of which correspond to the estimated directions and the root vertex corresponds to the complex assessment) and the matrix convolutions in each (non-hanging) tree vertex are selected. In addition, requirements are developed for grading scales, a convolution tree, and convolution matrices, which allow one to clearly demonstrate the synergistic effect and give a description of the class of generalized median schemes implemented on the convex representation of binary trees. Materials and methods. The solution to these problems is based on the method of synthesis and generalization of existing approaches to building a comprehensive assessment and simulation when choosing the structure of a complex assessment system. At the same time, convolution matrices are determined as training options set by experts, such that for any training option, the score obtained by the system of assessment coincides with the expert score. Results. The conditions for the existence of a matrix in the form of inequalities for generalized estimates of training sub-options are obtained. These inequalities are associated with a graph whose vertices denote sub-options, and arcs reflect inequalities connecting generalized estimates. It is shown that if the graph has no contours, then the convolution matrix exists. An algorithm is proposed for determining generalized estimates of sub variants based on the obtained graph and the subsequent determination of the corresponding matrix. The substantiation of the requirements for assessment scales and the structure of the convolution tree is given. The requirements are obtained for the dimension and type of convolution matrices located in the nodes of the tree. The possibility of implementing a set of training data using some kind of integrated assessment mechanism is determined. Conclusion. The results obtained make it possible to formulate integrated assessment systems that provide flexibility of tuning to the preferences of decision makers, ease of calculation, and the ability to solve optimization problems of program formation on this basis.Введение. Построение качественной комплексной оценки объекта требует более точного и всестороннего учета как объективной информации об объекте, так субъективных оценок, индивидуального опыта, интуиции и знаний руководителя объекта. Цель исследования. Целью работы является разработка современного подхода к решению задач многокритериального оценивания и ранжирования на основе формирования согласованной структуры комплексной оценки. Для этого осуществляется выбор структуры системы комплексного оценивания (дихотомического дерева, висячие вершины которого соответствуют оцениваемым направлениям, а корневая вершина – комплексной оценке) и выбор матричных сверток в каждой (не висячей) вершине дерева. Кроме того, разрабатываются требования к шкалам оценивания, дереву свертки и матрицам свертки, позволяющим наглядно продемонстрировать синергетический эффект и дать описание класса обобщенных медианных схем, реализуемых на выпуклом представлении двоичных деревьев. Материалы и методы. Решение этих задач основывается на методе синтеза и обобщения существующих подходов к построению комплексной оценки и имитационного моделирования при выборе структуры системы комплексного оценивания. При этом определены матрицы свертки на основе обучающих вариантов, задаваемых экспертами, такие, что для любого обучающего варианта оценка, полученная на основе системы комплексного оценивания, совпадает с экспертной оценкой. Результаты. Получены условия существования матрицы в виде неравенств на обобщенные оценки обучающих подвариантов. Этим неравенствам поставлен в соответствие граф, вершины которого обозначают подварианты, а дуги отражают неравенства, связывающие обобщенные оценки. Показано, что если граф не имеет контуров, то матрица свертки существует. Предложен алгоритм определения обобщенных оценок подвариантов на основе полученного графа и последующего определения соответствующей матрицы. Дано обоснование требований к шкалам оценивания и структуре дерева свертки. Получены требования к размерности и виду матриц свертки, находящихся в узлах дерева. Определена возможность реализации набора обучающих данных с помощью какого-либо механизма комплексного оценивания. Заключение. Поученные результаты позволяют формировать системы комплексного оценивания, обеспечивающие гибкость настройки на предпочтения лиц, принимающих решения, простоту расчетов и возможность решения на этой основе оптимизационных задач формирования программ

Multi-test Decision Tree and its Application to Microarray Data Classification

Objective: The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. Methods: We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Results: Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on $14$ datasets by an average $6$ percent. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. Conclusion: This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts

Random survival forests for predicting the bed occupancy in the intensive care unit

Predicting the bed occupancy of an intensive care unit (ICU) is a daunting task. The uncertainty associated with the prognosis of critically ill patients and the random arrival of new patients can lead to capacity problems and the need for reactive measures. In this paper, we work towards a predictive model based on Random Survival Forests which can assist physicians in estimating the bed occupancy. As input data, we make use of the Sequential Organ Failure Assessment (SOFA) score collected and calculated from 4098 patients at two ICU units of Ghent University Hospital over a time period of four years. We compare the performance of our system with a baseline performance and a standard Random Forest regression approach. Our results indicate that Random Survival Forests can effectively be used to assist in the occupancy prediction problem. Furthermore, we show that a group based approach, such as Random Survival Forests, performs better compared to a setting in which the length of stay of a patient is individually assessed