Role of Feature Selection in Building High Performance Heart Disease Prediction Systems

In the last few years, there has been a tremendous rise in the number of deaths due to heart diseases all over the world. In low- and middle-income countries, heart diseases are usually not detected in early stages which makes the treatment difficult. Early diagnosis can help significantly in preventing these diseases. Machine learning-based prediction systems offer a cost-effective and efficient way to diagnose these diseases in an early stage. Research is being carried out to increase the performance of these systems. Redundant and irrelevant features in the medical dataset deteriorate the performance of prediction systems. In this paper, an exhaustive study has been done to improve the performance of the prediction systems by applying 4 feature selection algorithms. Experimental results prove that the use of feature selection algorithms provides a substantial increase in accuracy and speed of execution of the prediction system. The prediction system proposed in this study shall prove to be a great help to prevent heart diseases by enabling the medical practitioners to detect heart diseases in early stages

Microload Management in Generation Constrained Power Systems

The reasons for power systems' outages can be complicated and difficult to pinpoint, but an obvious shortfall in generation compared to electricity demand has been identified as the major cause of load shedding in generation constrained power systems. A sudden rise in demand for electricity on these networks at any time could result in a total collapse of the entire grid. Therefore, in this thesis, algorithms to efficiently allocate the available generation are investigated to prevent the associated hardships and lose experience by the final consumers and the electric utility suppliers, respectively. Heuristic technique is utilised by developing various dynamic programming-based algorithms to achieve the constraints of uniquely controlling home appliances to reduce the overall demands for electricity by the consumers within the grid in context. These algorithms are focused on the consumers' comfort and the associated benefits to the electricity utility company in the long run. The evaluation of the proposed approach is achieved through microload management by employing three main techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS). These techniques were evaluated using both Grouped and “UnGrouped” microloads based on how efficient the microload managed the available generation to prevent total blackouts. A progressive reduction in excess microload shedding experienced by GS, PBS, and the ERS shows the proposed algorithms' effectiveness. Further, predictive algorithms are investigated for microload forecasting towards microload management to prepare both consumers and the electric utility companies for any impending load shedding. Measuring the forecasting accuracy and the root mean square errors of the models evaluated proved the potential for microload demand prediction

Análise remota do eletrocardiograma para detecção de eventos isquêmicos

The evolution of technologies for remote services delivery over the Internet unveils a new frontier in the development of the knowledge needed to implement health prevention measures. In this study, a computational tool was conceived for the remote analysis of multiple lead electrocardiograms. As a proof of concept, a method for detecting ST-T segment changes related to ischemic episodes in remote computing is proposed. The architecture combines only open source software that allows incremental object-oriented programming and support multiuser services via the Web, focusing on system evolution within the academic world. The technique used to detect ischemic events favored low computational cost and storage of both data and metadata in a database. It was anchored in a method of interpolation by weighted least squares and histograms, capable of detecting the positions of the QRS complexes, and the respective positions of J points and T waves. These points were used as borderline positions in obtaining representative under curve areas for the subsequent detection of ischemic events in the leads present in the research file. After assessment with engineering students, we conclude that the platform, architecture, and programming techniques provide a satisfactory tool for ischemic event management that can be used to develop new biomedical signal processing techniques that support the risk assessment of myocardial dysfunction.A evolução das tecnologias para entrega de serviços remotos pela Internet revela uma nova fronteira no desenvolvimento do conhecimento necessário para implementar medidas de prevenção da saúde. Neste estudo, uma ferramenta computacional foi concebida para a análise remota de eletrocardiogramas de múltiplas derivações. Como prova de conceito, um método é proposto para detectar alterações no segmento ST-T relacionadas a episódios isquêmicos através da computação remota. A arquitetura combina apenas software de código aberto que permite programação incremental orientada a objetos e oferece suporte a serviços multiusuário via Web, com foco na evolução do sistema no mundo acadêmico. A técnica utilizada para detectar eventos isquêmicos favoreceu o baixo custo computacional e armazenamento de dados e metadados em um Banco de Dados. Foi ancorado em um método de interpolação por mínimos quadrados ponderados e histogramas, capazes de detectar as posições dos complexos QRS e as respectivas posições dos pontos J e ondas T. Esses pontos foram usadas como posições limítrofes na obtenção de áreas representativas sob curvas para a subsequente detecção de eventos isquêmicos nas derivações presentes no arquivo de pesquisa. Após avaliação junto a discentes de engenharia, concluímos que a plataforma, arquitetura e técnicas de programação fornecem uma ferramenta satisfatória para o gerenciamento de eventos isquêmicos, a qual pode ser usada para o desenvolvimento de novas técnicas de processamento de sinais biomédicos que objetivem apoiar a avaliação de risco de disfunção miocárdic