An Extensive Investigation on Coronory Heart Disease using Various Neuro Computational Models

The diagnosis of heart disease at the early time is important to save the life of people as it is absolutely annoying process which requires extent knowledge and rich experience. By and large the expectation of heart infections in conventional method for inspecting reports, for example, Electrocardiogram-ECG, Magnetic Resonance Imaging- MRI, Blood Pressure-BP, Stress tests by medicinal professionals. Presently a-days a huge volume of therapeutic information is accessible in restorative industry in all maladies and these truths goes about as an incredible source in foreseeing the coronary illness by the professionals took after by appropriate ensuing treatment at an early stage can bring about noteworthy life sparing. There are numerous systems in ANN ideas which are likewise contributing themselves in yielding most elevated expectation precision over medical information. As of late, a few programming devices and different techniques have been proposed by analysts for creating powerful decision supportive systems. More over many new tools and algorithms are continued to develop and representing the old ones day by day. This paper aims the study of such different methods by researchers with high accuracy in predicting the heart diseases and more study should go on to improve the accuracy over predictions of heart diseases using Neuro Computing

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013

Predictive analytics applied to Alzheimer’s disease : a data visualisation framework for understanding current research and future challenges

Dissertation as a partial requirement for obtaining a master’s degree in information management, with a specialisation in Business Intelligence and Knowledge Management.Big Data is, nowadays, regarded as a tool for improving the healthcare sector in many areas, such as in its economic side, by trying to search for operational efficiency gaps, and in personalised treatment, by selecting the best drug for the patient, for instance. Data science can play a key role in identifying diseases in an early stage, or even when there are no signs of it, track its progress, quickly identify the efficacy of treatments and suggest alternative ones. Therefore, the prevention side of healthcare can be enhanced with the usage of state-of-the-art predictive big data analytics and machine learning methods, integrating the available, complex, heterogeneous, yet sparse, data from multiple sources, towards a better disease and pathology patterns identification. It can be applied for the diagnostic challenging neurodegenerative disorders; the identification of the patterns that trigger those disorders can make possible to identify more risk factors, biomarkers, in every human being. With that, we can improve the effectiveness of the medical interventions, helping people to stay healthy and active for a longer period. In this work, a review of the state of science about predictive big data analytics is done, concerning its application to Alzheimer’s Disease early diagnosis. It is done by searching and summarising the scientific articles published in respectable online sources, putting together all the information that is spread out in the world wide web, with the goal of enhancing knowledge management and collaboration practices about the topic. Furthermore, an interactive data visualisation tool to better manage and identify the scientific articles is develop, delivering, in this way, a holistic visual overview of the developments done in the important field of Alzheimer’s Disease diagnosis.Big Data é hoje considerada uma ferramenta para melhorar o sector da saúde em muitas áreas, tais como na sua vertente mais económica, tentando encontrar lacunas de eficiência operacional, e no tratamento personalizado, selecionando o melhor medicamento para o paciente, por exemplo. A ciência de dados pode desempenhar um papel fundamental na identificação de doenças em um estágio inicial, ou mesmo quando não há sinais dela, acompanhar o seu progresso, identificar rapidamente a eficácia dos tratamentos indicados ao paciente e sugerir alternativas. Portanto, o lado preventivo dos cuidados de saúde pode ser bastante melhorado com o uso de métodos avançados de análise preditiva com big data e de machine learning, integrando os dados disponíveis, geralmente complexos, heterogéneos e esparsos provenientes de múltiplas fontes, para uma melhor identificação de padrões patológicos e da doença. Estes métodos podem ser aplicados nas doenças neurodegenerativas que ainda são um grande desafio no seu diagnóstico; a identificação dos padrões que desencadeiam esses distúrbios pode possibilitar a identificação de mais fatores de risco, biomarcadores, em todo e qualquer ser humano. Com isso, podemos melhorar a eficácia das intervenções médicas, ajudando as pessoas a permanecerem saudáveis e ativas por um período mais longo. Neste trabalho, é feita uma revisão do estado da arte sobre a análise preditiva com big data, no que diz respeito à sua aplicação ao diagnóstico precoce da Doença de Alzheimer. Isto foi realizado através da pesquisa exaustiva e resumo de um grande número de artigos científicos publicados em fontes online de referência na área, reunindo a informação que está amplamente espalhada na world wide web, com o objetivo de aprimorar a gestão do conhecimento e as práticas de colaboração sobre o tema. Além disso, uma ferramenta interativa de visualização de dados para melhor gerir e identificar os artigos científicos foi desenvolvida, fornecendo, desta forma, uma visão holística dos avanços científico feitos no importante campo do diagnóstico da Doença de Alzheimer

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way

Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy

Implementation of a 3D CNN for COPD classification

Segons les prediccions de la Organització Mundial de la Salut (OMS) pels voltants del 2030 la Malaltia Pulmonar Obstructiva Crònica (MPOC) es convertirá en la tercera causa de mort en tot el món. L’MPOC és una patologia que afecta a les vies respiratòries i als pulmons. Avui en dia esdevé crónica i incurable però, és una malaltia tractable i prevenible. Fins ara les proves de diagnòstic usades per a detectar l’MPOC es basen en l’espirometria, aquesta prova, tot i indicar el grau d’obstrucció al pas de l’aire que es produeix en els pulmons, sovint no és molt fiable. És per aquest motiu que s’estan començant a usar tècniques basades en algorismes de Deep Learning per a la classificaió més acurada d’aquesta patologia, basant-se en imatges tomogràfiques de pacients malalts d’MPOC. Les xarxes neuronals convolucionals en tres dimensions (3D-CNN) en són un exemple. A partir de les dades i les imatges obtingudes en l’estudi observacional d’ECLIPSE proporcionades per l’equip de recerca de BRGE de ISGlobal, s’implementa una 3D-CNN per a la classificació de pacients amb risc d’MPOC. Aquest treball té com a objectiu desenvolupar una recerca extensa sobre la recerca actual en aquest àmbit i proposa millores per a l’optimització i reducció del cost computacional d’una 3D-CNN per aquest cas d’estudi concret.Según las predicciones de la Organización Mundial de la Salud (OMS), para alrededor del 2030, la Enfermedad Pulmonar Obstructiva Crónica (EPOC) se convertirá en la tercera causa de muerte en todo el mundo. La EPOC es una enfermedad que afecta las vías respiratorias y los pulmones. En la actualidad, se considera crónica e incurable, pero es una enfermedad tratable y prevenible. Hasta ahora, las pruebas de diagnóstico utilizadas para detectar la EPOC se basan en la espirometría. Esta prueba, a pesar de indicar el grado de obstrucción en el flujo de aire que ocurre en los pulmones, a menudo no es muy confiable. Es por esta razón que se están empezando a utilizar técnicas basadas en algoritmos de Deep Learning para una clasificación más precisa de esta patología, utilizando imágenes tomográficas de pacientes enfermos de EPOC. Las redes neuronales convolucionales en tres dimensiones (3D-CNN) son un ejemplo de esto. A partir de los datos y las imágenes obtenidas en el estudio observacional ECLIPSE proporcionado por el equipo de investigación de BRGE de ISGlobal, se implementa una 3D-CNN para la clasificación de pacientes con riesgo de EPOC. Este trabajo tiene como objetivo desarrollar una investigación exhaustiva sobre la investigación actual en este campo y propone mejoras para la optimización y reducción del costo computacional de una 3D-CNN para este caso de estudio concreto.According to predictions by the World Health Organization (WHO), by around 2030, Chronic Obstructive Pulmonary Disease (COPD) will become the third leading cause of death worldwide. COPD is a condition that affects the respiratory tract and lungs. Currently, it is considered chronic and incurable, but it is a treatable and preventable disease. Up to now, diagnostic tests used to detect COPD have been based on spirometry. Despite indicating the degree of airflow obstruction in the lungs, this test is often not very reliable. That is why techniques based on Deep Learning algorithms are being increasingly used for more accurate classification of this pathology, based on tomographic images of COPD patients. Three-dimensional Convolutional Neural Networks (3D-CNN) are an example of such techniques. Based on the data and images obtained in the observational study called ECLIPSE, provided by the research team at BRGE of ISGlobal, a 3D-CNN is implemented for the classification of patients at risk of COPD. This work aims to conduct extensive research on the current state of research in this field and proposes improvements for the optimization and reduction of the computational cost of a 3D-CNN for this specific case study