Comparative Analysis of the C4.5 and ID3 Decision Tree Algorithms for Disease Symptom Classification and Diagnosis

Most disease surveillance outfits and authrorities around the world battle with one key challenge – the useful and objective handling and processing of the huge sets of disease data being generated on a regular basis as their personnel exercise their disease surveillance mandate. Many theories have been put forth on how best this could be tackled. Among these, is the use of information technology and mathematical theories and concepts to alleviate the problem. One of the most solid and promising methods includes the use of artificial intelligence techniques to help break down and make good sense of the data sets. This research looks to compare the usage of the C4.5 and the ID3 decision tree theory concepts as means of tackling making the best of disease surveillance data. The C4.5 and ID3 algorithms provide a method of breaking down the data and generating (among other useful information) the entropies and information gains of some predefined variables from huge sets of disease outbreak data. Once the information gain scores for the variables are computed, they can be easily ranked to determine the variable to define the root node in the decision tree, as the rest of the variables follow through as leaf nodes. Notably, there will be two sets of entropies and information gains; one from the C4.5 algortihm and the other from the ID3 algorithm. Both decision trees shall have validation steps after each branch pass to determine whether it is time to stop growing it or not. This is one of the mechanisms employed here to avoid overfiting of the decision tree (especially for the ID3 algorithm)

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery

PENDEKATAN METODE POHON KEPUTUSAN MENGGUNAKAN ALGORITMA ITERATIVE DICHOTOMIZER (ID3) UNTUK PENGUKURAN KINERJA PEGAWAI NEGERI SIPIL

Decision tree method is a classification method that has been widely used for the solution of problems of classification. Decision tree classification provides a rapid and effective method. The approach has been proven decision tree method can be applied in various fields of life. Capability classification is indicated by the decision tree method is what encourages authors to use decision tree methods approach to measure the performance of civil servants.  To build a decision tree induction algorithms used. In this study, the ID3 algorithm method is used to construct a decision tree. Starting with the data collecting training samples and then measuring the entropy and information gain. Information Gain value will be used as the root of a decision tree. And translates it into a decision tree classification rules.The results show that the decision tree method is used to produce classification rules into groups employee performance Good and Bad. The resulting rules are used to measure the performance of employees and classifying employees into two groups.The result to assist management in making more objective assessment process. Keywords: ID3 Algorithm, Decision Tree, Employee Performance

Survey on representation techniques for malware detection system

Malicious programs are malignant software’s designed by hackers or cyber offenders with a harmful intent to disrupt computer operation. In various researches, we found that the balance between designing an accurate architecture that can detect the malware and track several advanced techniques that malware creators apply to get variants of malware are always a difficult line. Hence the study of malware detection techniques has become more important and challenging within the security field. This review paper provides a detailed discussion and full reviews for various types of malware, malware detection techniques, various researches on them, malware analysis methods and different dynamic programmingbased tools that could be used to represent the malware sampled. We have provided a comprehensive bibliography in malware detection, its techniques and analysis methods for malware researchers

Computational Approaches for Remote Monitoring of Symptoms and Activities

We now have a unique phenomenon where significant computational power, storage, connectivity, and built-in sensors are carried by many people willingly as part of their life style; two billion people now use smart phones. Unique and innovative solutions using smart phones are motivated by rising health care cost in both the developed and developing worlds. In this work, development of a methodology for building a remote symptom monitoring system for rural people in developing countries has been explored. Design, development, deployment, and evaluation of e-ESAS is described. The system’s performance was studied by analyzing feedback from users. A smart phone based prototype activity detection system that can detect basic human activities for monitoring by remote observers was developed and explored in this study. The majority voting fusion technique, along with decision tree learners were used to classify eight activities in a multi-sensor framework. This multimodal approach was examined in details and evaluated for both single and multi-subject cases. Time-delay embedding with expectation-maximization for Gaussian Mixture Model was explored as a way of developing activity detection system using reduced number of sensors, leading to a lower computational cost algorithm. The systems and algorithms developed in this work focus on means for remote monitoring using smart phones. The smart phone based remote symptom monitoring system called e-ESAS serves as a working tool to monitor essential symptoms of patients with breast cancer by doctors. The activity detection system allows a remote observer to monitor basic human activities. For the activity detection system, the majority voting fusion technique in multi-sensor architecture is evaluated for eight activities in both single and multiple subjects cases. Time-delay embedding with expectation-maximization algorithm for Gaussian Mixture Model was studied using data from multiple single sensor cases

Emerg Infect Dis

PMC4550154611

Distributed Online Machine Learning for Mobile Care Systems

Appendix D: Wavecomm Tech Docs removed for copyright reasonsTelecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and even health outcomes for patients. In addition, they allow significant cost savings for adult care by reducing the needs for medical staff. A common drawback of current Mobile Care Systems is that they are rather stationary in most cases and firmly installed in patients’ houses or flats, which makes them stay very near to or even in their homes. There is also an upcoming second category of Mobile Care Systems which are portable without restricting the moving space of the patients, but with the major drawback that they have either very limited computational abilities and only a rather low classification quality or, which is most frequently, they only have a very short runtime on battery and therefore indirectly restrict the freedom of moving of the patients once again. These drawbacks are inherently caused by the restricted computational resources and mainly the limitations of battery based power supply of mobile computer systems. This research investigates the application of novel Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the operation of 2 Mobile Care Systems. As a result, based on the Evolving Connectionist Systems (ECoS) paradigm, an innovative approach for a highly efficient and self-optimising distributed online machine learning algorithm called MECoS - Moving ECoS - is presented. It balances the conflicting needs of providing a highly responsive complex and distributed online learning classification algorithm by requiring only limited resources in the form of computational power and energy. This approach overcomes the drawbacks of current mobile systems and combines them with the advantages of powerful stationary approaches. The research concludes that the practical application of the presented MECoS algorithm offers substantial improvements to the problems as highlighted within this thesis

Emerging infectious diseases

Emerging Infectious Diseases is providing access to these abstracts on behalf of the ICEID 2012 program committee (www.iceid.org), which performed peer review. Emerging Infectious Diseases has not edited or proofread these materials and is not responsible for inaccuracies or omissions. All information is subject to change. Comments and corrections should be brought to the attention of the authors.Influenza preparedness: lessons learned -- Policy implications and infectious diseases -- Improving preparedness for infectious diseases -- New or rapid diagnostics -- Foodborne and waterborne infections -- Effective and sustainable surveillance platforms -- Healthcare-associated infections -- Molecular epidemiology -- Antimicrobial resistance -- Tropical infections and parasitic diseases -- H1N1 influenza -- Risk Assessment -- Laboratory Support -- Zoonotic and Animal Diseases -- Viral Hepatitis -- E1. Zoonotic and animal diseases -- E2. Vaccine issues -- E3. H1N1 influenza -- E4. Novel surveillance systems -- E5. Antimicrobial resistance -- E6. Late-breakers I -- Antimicrobial resistance -- Influenza preparedness: lessons learned -- Zoonotic and animal diseases -- Improving preparedness for infectious diseases -- Laboratory support -- Early warning systems -- H1N1 influenza -- Policy implications and infectious diseases -- Modeling -- Molecular epidemiology -- Novel surveillance systems -- Tropical infections and parasitic diseases -- Strengthening public health systems -- Immigrant and refugee health -- Foodborne and waterborne infections -- Healthcare-associated infections -- Foodborne and waterborne infections -- New or rapid diagnostics -- Improving global health equity for infectious diseases -- Vulnerable populations -- Novel agents of public health importance -- Influenza preparedness: lessons learned -- Molecular epidemiology -- Zoonotic and animal diseases -- Vaccine-preventable diseases -- Outbreak investigation: lab and epi response -- H1N1 influenza -- laboratory support -- effective and sustainable surveillance platforms -- new vaccines -- vector-borne diseases and climate change -- travelers' health -- J1. Vectorborne diseases and climate change -- J2. Policy implications and infectious diseases -- J3. Influenza preparedness: lessons learned -- J4. Effective and sustainable surveillance platforms -- J5. Outbreak investigation: lab and epi response I -- J6. Late-breakers II -- Strengthening public health systems -- Bacterial/viral coinfections -- H1N1 influenza -- Novel agents of public health importance -- Foodborne and waterborne infections -- New challenges for old vaccines -- Vectorborne diseases and climate change -- Novel surveillance systems -- Geographic information systems (GIS) -- Improving global health equity for infectious diseases -- Vaccine preventable diseases -- Vulnerable populations -- Laboratory support -- Prevention challenges for respiratory diseases -- Zoonotic and animal diseases -- Outbreak investigation: lab and epi response -- Vectorborne diseases and climate change -- Outbreak investigation: lab and epi response -- Laboratory proficiency testing/quality assurance -- Effective and sustainable surveillance platforms -- Sexually transmitted diseases -- H1N1 influenza -- Surveillance of vaccine-preventable diseases -- Foodborne and waterborne infections -- Role of health communication -- Emerging opportunistic infections -- Host and microbial genetics -- Respiratory infections in special populations -- Zoonotic and animal diseases -- Laboratory support -- Antimicrobial resistance -- Vulnerable populations -- Global vaccine initiatives -- Tuberculosis -- Prevention challenges for respiratory diseases -- Infectious causes of chronic diseases -- O1. Outbreak investigation: lab and epi response II -- O2. Prevention challenges for respiratory diseases -- O3. Populations at high risk for infectious diseases -- O4. Foodborne and waterborne infections -- O5. Laboratory support: surveillance and monitoring infections -- O6. Late-breakers IIIAbstracts published in advance of the conference

Emerg Infect Dis

Emerging Infectious Diseases is providing access to these abstracts on behalf of the ICEID 2022 program committee (http://www.iceid.org), which performed peer review. ICEID is organized by the Centers for Disease Control and Prevention and Task Force for Global Health, Inc.Emerging Infectious Diseases has not edited or proofread these materials and is not responsible for inaccuracies or omissions. All information is subject to change. Comments and corrections should be brought to the attention of the authors.Suggested citation: Authors. Title [abstract]. International Conference on Emerging Infectious Diseases 2022 poster and oral presentation abstracts. Emerg Infect Dis. 2022 Sep [date cited]. http://www.cdc.gov/EID/pdfs/ICEID2022.pdf2022PMC94238981187