9 research outputs found
Hybrid System of Tiered Multivariate Analysis and Artificial Neural Network for Coronary Heart Disease Diagnosis
Improved system performance diagnosis of coronary heart disease becomes an important topic in research for several decades. One improvement would be done by features selection, so only the attributes that influence is used in the diagnosis system using data mining algorithms. Unfortunately, the most feature selection is done with the assumption has provided all the necessary attributes, regardless of the stage of obtaining the attribute, and cost required. This research proposes a hybrid model system for diagnosis of coronary heart disease. System diagnosis preceded the feature selection process, using tiered multivariate analysis. The analytical method used is logistic regression. The next stage, the classification by using multi-layer perceptron neural network. Based on test results, system performance proposed value for accuracy 86.3%, sensitivity 84.80%, specificity 88.20%, positive prediction value (PPV) 90.03%, negative prediction value (NPV) 81.80%, accuracy 86,30% and area under the curve (AUC) of 92.1%. The performance of a diagnosis using a combination attributes of risk factors,symptoms and exercise ECG. The conclusion that can be drawn is that the proposed diagnosis system capable of delivering performance in the very good category, with a number of attributes that are not a lot of checks and a relatively low cost
Identificación de relaciones entre los nodos de una red social
In this paper a review is conduced about representation and classifi cation of membership among nodes belonging to a social network. For this purpose, topics such as Natural Language Processing, Text Mining, Information Retrieval and Named Entities are considered description and survey of outstanding approaches is carry out in each topic.El presente artículo realiza una revisión del tema, representación y clasificación de de relaciones de pertenencia entre los nodos de una red social. Para ello, se abordan aspectos sobre Procesamiento de Lenguaje Natural, Minería de Texto, Recuperación de Informacióny Entidades Nombradas. Se hace una descripción de cada una de ellas y se referencian y discuten trabajos académicos destacados que se han desarrollado en dicho tema
Evolución y tendencias actuales de los Web crawlers
The information stored through the social network services is a growing source of information with special dynamic characteristics. The mechanisms responsible for tracking changes in such information (Web crawlers) often must be studied, and it is necessary to review and improve their algorithms. This document presents the current status of tracking algorithms of the Web (Web crawlers), its trends and developments, and its approach towards managing challenges emerging like social networks.La información disponible en redes de datos como la Web o las redes sociales se encuentra en continuo crecimiento, con unas características de dinamismo especiales. Entre los mecanismos encargados de rastrear los cambios en dicha información se encuentran los Webcrawlers, los cuales por la misma dinámica de la información, deben mejorarse constantemente en busca de algoritmos más eficientes. Este documento presenta el estado actual de los algoritmos de rastreo de la Web, sus tendencias, avances, y nuevos enfoques dentro del contexto de la dinámica de las redes sociales
An intelligent Medical Cyber-Physical System to support heart valve disease screening and diagnosis
Cardiovascular diseases are currently the major causes of death globally. Among the strategies to prevent cardiovascular issues, the automated classification of heart sound abnormalities is an efficient way to detect early signs of cardiac conditions leading to heart failure or other, even asymptomatic, complications, quite effective for timely interventions. Despite the significant improvements in this field, there are still limitations due to the lack of solutions, available data-sets and poor (mainly binary - normal vs abnormal) classification models and algorithms. This paper presents a Medical Cyber-Physical System (MCPS) for the automatic classification of heart valve diseases onsite, in a timely manner. The proposed MCPS, indeed, can be deployed into personal and mobile devices, addressing the limitations of existing solutions for patients, healthcare practitioners, and researchers, through an efficient and easy accessible tool. It combines different neural network models trained on a new Italian dataset of 132 adult patients covering 9 heart sound categories (1 normal and 8 abnormal), also validated against two main open-access (Physionet/CinC Challenge 2016 and Korean) datasets. The overall MCPS performance (time, processing and energy resource utilization) and the high accuracy of the models (up to 98%) demonstrated the feasibility of the proposed solution, even with few data. The dataset supporting the findings of this paper is available upon request to the authors
Video Categorization Using Data Mining
Video categorization using data mining is the area of the research that aims to propose adeveloped method based on Artificial Neural Network (ANN), which could be used to classify video files into different categories according to the content. In order to test this method, the classifications of video files are discussed. The applied system proposes that the video could be categorized in two classes. The first one is educational while is noneducational. The classification is conducted based on the motion using optical flow. Several experiments were conducted using Artificial Neural Network (ANN) model. The research facilitate access to the required educational video to the learners students, especially novice students. This research objective is to investigate how the effect of motion feature can be useful in such lassification. We believe that other effects such audio features, text features, and other factors can enhance accuracy, but this requires wider studies and need more time. The accuracy of results in video classification to educational and non-educational through technique 3 fold cross validation and using (ANN) model is 54%. This result may can be improved by introducing other factors mentioned above
Comparison of Data Mining and Statistical Techniques for Prediction Model
The aim of this research is to perform a comparison study between statistical and data mining modeling techniques. These techniques are statistical Logistic Regression, data mining Decision Tree and data mining Neural Network. The performance of these prediction techniques were measured and compared in terms of measuring the overall prediction accuracy percentage agreement for each technique and the models were trained using eight different training datasets samples drawn using two different sampling techniques. The effect of the dependent variable values distribution in the training dataset
on the overall prediction percent and on the prediction accuracy of individual “0” and “1” values of the dependent variable values was also experimented. For a given data set, the results shows that the performance of the three techniques were comparable in general with small outperformance for the Neural Network. An affecting factor that makes the percent prediction accuracy varied is the dependent variable values distribution in the training dataset, distribution of “0” and “1”. The results showed that, for all the three techniques, the overall prediction accuracy percentage agreement was high when the dependent variable values distribution ratio in the training data was greater than 1:1 but at the same
time they, the techniques, fails to predict the individual dependent variable values successfully or in acceptable prediction percent. If the individual dependent variable values needed to be predicted comparably, then the dependent variable values distribution ratio in the training data should be exactly 1:1.هدف هذه الدراسة هو إجراء مقارنة الكفاءة والفعالية بين الوسائل اإلحصائية وتقنيات التنقيب عن البيانات لبناء نماذج
التصنيف والتنبؤ العلمي. الخوارزميات والوسائل والتقنيات التي تمت دراستها ومقارنة أدائها هي االنحدار اللوجستي
اإلحصائي، وتقنيتي التنقيب عن البيانات شجرة القرار والشبكة العصبية. تم قياس أداء هذه التقنيات ومقارنتها باالعتماد
على مقياس مشترك وهو النسبة المئوية الشاملة لدقة التنبؤ لكل تقنية. تم تدريب نماذج هذه التقنيات باستخدام ثمانية
عينات من بيانات التدريب تم سحبها باالعتماد على تقنيتي سحب عينات إحصائية. تم أيضا فحص تأثير توزيع قيم
المتغير التابع في بيانات تدريب خوارزميات التنبؤ المذكورة وذلك على مستوى النسبة المئوية الشاملة لدقة التنبؤ لكل
تقنية وأيضا على مستوى النسبة المئوية لدقة التنبؤ لقيم المتغير التابع الفردية "0 "و "1 "لكل تقنية. أظهرت النتائج أن
أداء التقنيات الثالثة كانت بشكل عام متقاربة وقابلة للمقارنة مع تفوق بسيط لخوارزمية الشبكات العصبية. تم تحديد
عنصر مؤثر على اختالف وتفاوت دقة النسبة المئوية للتنبؤ وهذا العنصر هو توزيع قيم المتغير التابع في بيانات
تدريب النماذج، أي توزيع "0 "و "1 ."كما أظهرت النتائج أيضا أن النسبة المئوية لدقة التنبؤ الشامل للتقنيات الثالثة
كانت مرتفعة عندما كانت نسبة توزيع قيم المتغير التابع في بيانات التدريب أكبر من 1:1 ولكن في الوقت نفسه فشلت
الخوارزميات والتقنيات قيد الدراسة في التنبؤ بالقيم الفردية للمتغير التابع بنجاح أو بنسبة تنبؤ مقبولة. في التطبيقات
باستخدام هذه التقنيات إذا كان الهدف هو الحصول على تنبؤ بنسبة مئوية عالية لقيم المتغير التابع الفردية وأن تكون
النسبة المئوية للتنبؤ بالقيمتين متقاربة فانه يجب أن تكون نسبة توزيع قيم المتغير التابع في بيانات التدريب بالضبط
.1:1 تساو
Information technologies for pain management
Millions of people around the world suffer from pain, acute or chronic and this raises the
importance of its screening, assessment and treatment. The importance of pain is attested by
the fact that it is considered the fifth vital sign for indicating basic bodily functions, health
and quality of life, together with the four other vital signs: blood pressure, body
temperature, pulse rate and respiratory rate. However, while these four signals represent an
objective physical parameter, the occurrence of pain expresses an emotional status that
happens inside the mind of each individual and therefore, is highly subjective that makes
difficult its management and evaluation. For this reason, the self-report of pain is considered
the most accurate pain assessment method wherein patients should be asked to periodically
rate their pain severity and related symptoms. Thus, in the last years computerised systems
based on mobile and web technologies are becoming increasingly used to enable patients to
report their pain which lead to the development of electronic pain diaries (ED). This approach
may provide to health care professionals (HCP) and patients the ability to interact with the
system anywhere and at anytime thoroughly changes the coordinates of time and place and
offers invaluable opportunities to the healthcare delivery. However, most of these systems
were designed to interact directly to patients without presence of a healthcare professional
or without evidence of reliability and accuracy. In fact, the observation of the existing
systems revealed lack of integration with mobile devices, limited use of web-based interfaces
and reduced interaction with patients in terms of obtaining and viewing information. In
addition, the reliability and accuracy of computerised systems for pain management are
rarely proved or their effects on HCP and patients outcomes remain understudied.
This thesis is focused on technology for pain management and aims to propose a monitoring
system which includes ubiquitous interfaces specifically oriented to either patients or HCP
using mobile devices and Internet so as to allow decisions based on the knowledge obtained
from the analysis of the collected data. With the interoperability and cloud computing
technologies in mind this system uses web services (WS) to manage data which are stored in a
Personal Health Record (PHR).
A Randomised Controlled Trial (RCT) was implemented so as to determine the effectiveness
of the proposed computerised monitoring system. The six weeks RCT evidenced the
advantages provided by the ubiquitous access to HCP and patients so as to they were able to
interact with the system anywhere and at anytime using WS to send and receive data. In
addition, the collected data were stored in a PHR which offers integrity and security as well
as permanent on line accessibility to both patients and HCP. The study evidenced not only
that the majority of participants recommend the system, but also that they recognize it
suitability for pain management without the requirement of advanced skills or experienced users. Furthermore, the system enabled the definition and management of patient-oriented
treatments with reduced therapist time. The study also revealed that the guidance of HCP at
the beginning of the monitoring is crucial to patients' satisfaction and experience stemming
from the usage of the system as evidenced by the high correlation between the
recommendation of the application, and it suitability to improve pain management and to
provide medical information. There were no significant differences regarding to
improvements in the quality of pain treatment between intervention group and control group.
Based on the data collected during the RCT a clinical decision support system (CDSS) was
developed so as to offer capabilities of tailored alarms, reports, and clinical guidance. This
CDSS, called Patient Oriented Method of Pain Evaluation System (POMPES), is based on the
combination of several statistical models (one-way ANOVA, Kruskal-Wallis and Tukey-Kramer)
with an imputation model based on linear regression. This system resulted in fully accuracy
related to decisions suggested by the system compared with the medical diagnosis, and
therefore, revealed it suitability to manage the pain. At last, based on the aerospace systems
capability to deal with different complex data sources with varied complexities and
accuracies, an innovative model was proposed. This model is characterized by a qualitative
analysis stemming from the data fusion method combined with a quantitative model based on
the comparison of the standard deviation together with the values of mathematical
expectations. This model aimed to compare the effects of technological and pen-and-paper
systems when applied to different dimension of pain, such as: pain intensity, anxiety,
catastrophizing, depression, disability and interference. It was observed that pen-and-paper
and technology produced equivalent effects in anxiety, depression, interference and pain
intensity. On the contrary, technology evidenced favourable effects in terms of
catastrophizing and disability. The proposed method revealed to be suitable, intelligible, easy
to implement and low time and resources consuming. Further work is needed to evaluate the
proposed system to follow up participants for longer periods of time which includes a
complementary RCT encompassing patients with chronic pain symptoms. Finally, additional
studies should be addressed to determine the economic effects not only to patients but also
to the healthcare system