This paper addresses the problem of forecasting daily stock trends. The key consideration is to predict whether a given stock will close on uptrend tomorrow with reference to today’s closing price. We propose a forecasting model that comprises a features selection model, based on the Genetic Algorithm (GA), and Random Forest (RF) classifier. In our study, we consider four international stock indices that follow the concept of distributed lag analysis. We adopted a genetic algorithm approach to select a set of helpful features among these lags’ indices. Subsequently, we employed the Random Forest classifier, to unveil hidden relationships between stock indices and a particular stock’s trend. We tested our model by using it to predict the trends of 15 stocks. Experiments showed that our forecasting model had 80% accuracy, significantly outperforming the dummy forecast. The S&P 500 was the most useful stock index, whereas the CAC40 was the least useful in the prediction of daily stock trends. This study provides evidence of the usefulness of employing international stock indices to predict stock trends.
PurposeThe purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of this new conceptual framework is to improve the health conditions in a dynamic region characterized by the appearance of new diseases.Design/methodology/approachThis study presents a new conceptual framework that could be employed in the European Mediterranean healthcare sector. Practically, this study can enhance medical services, taking smart decisions based on accurate data for healthcare and, finally, reducing the medical treatment costs, thanks to data quality control.FindingsThis research proposes a new conceptual framework for BDA in the healthcare sector that could be integrated in the European Mediterranean region. This framework introduces the big data quality (BDQ) module to filter and clean data that are provided from different European data sources. The BDQ module acts in a loop mode where bad data are redirected to their data source (e.g. European Centre for Disease Prevention and Control, university hospitals) to be corrected to improve the overall data quality in the proposed framework. Finally, clean data are directed to the BDA to take quick efficient decisions involving all the concerned stakeholders.Practical implicationsThis study proposes a new conceptual framework for executives in the healthcare sector to improve the decision-making process, decrease operational costs, enhance management performance and save human lives.Originality/valueThis study focused on big data management and BDQ in the European Mediterranean healthcare sector as a broadly considered fundamental condition for the quality of medical services and conditions.
The integration of genomics and patient related data is considered as one of the most promising investigation topic in health care research. Started in 2004, the Grid for Geno Medicine (GGM) project aims at providing a comprehensive grid software infrastructure designed to allow biologists to mine and analyze relationships between medical, genetic, and genomic data stored in distributed datawarehouses. The proposed layered service oriented architecture offers a number of independent but compliant services that can be deployed in a grid environment. This paper presents these services insisting on their integration into a common software platform, the use case that is carried out. It also presents the current state of the developments and of the performance evaluations.
Blockchain technology offers great properties such as scalability, decentralization, immutability, and security. These properties attracted many new fields such as healthcare, education, finance, and much more. This chapter proposes combining blockchain with big data technologies in order to provide a smart and efficient healthcare model. First, the authors propose to use the blockchain to save medical records (transactions) for patients on a large-scale system while preserving the privacy for patient. Blockchain can be used to keep track about each medical record for a patient. Since blockchains can save small amounts of data and since medical records can become very lengthy and complex to analyze, the authors propose to use big data technology in combination with blockchain. The big data technology can store large files and can handle heterogeneous files. Big data technology can apply sophisticated analysis on the data collected from blockchain.
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