At What Price? Exploring the Potential and Challenges of Differentially Private Machine Learning for Healthcare

2023 | HICSS | Citations: 1

Authors: Aslan, Aycan; Matschak, Tizian; Greve, Maike; Trang, Simon; Kolbe, Lutz

Abstract: The increased generation of data has become one of the main drivers of technolog ... Expand

Abstract: The increased generation of data has become one of the main drivers of technological innovation in healthcare. This applies in particular to the adoption of Machine Learning models that are used to generate value from the growing available healthcare data. However, the increased processing of sensitive healthcare data comes with challenges in terms of data privacy. Differential privacy, the method of adding randomness to the data to increase privacy, has gained popularity in the last few years as a possible solution. However, while the addition of randomness increases privacy, it also reduces overall model performance, generating a privacy-utility trade-off. Examining this trade-off, we contribute to the literature by providing an empirical paper that experimentally evaluates two prominent and innovative methods of differentially private Machine Learning on medical image and text data to deepen the understanding of the existing potential and challenges of such methods for the healthcare domain. Collapse

Topics: privacy differential privacy healthcare data knowledge base data protection

Methods: experiment computational algorithm machine learning blood pressure PyTorch

COVIDAL: A Machine Learning Classifier for Digital COVID-19 Diagnosis in German Hospitals

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Bartenschlager, Christina C.; Ebel, Stefanie S.; Kling, Sebastian; Vehreschild, Janne; Zabel, Lutz T.; Spinner, Christoph D.; Schuler, Andreas; Heller, Axel R.; Borgmann, Stefan; Hoffmann, Reinhard; Rieg, Siegbert; Messmann, Helmut; Hower, Martin; Brunner, Jens O.; Hanses, Frank; Römmele, Christoph

Abstract: For the fight against the COVID-19 pandemic, it is particularly important to map ... Expand

Abstract: For the fight against the COVID-19 pandemic, it is particularly important to map the course of infection, in terms of patients who have currently tested SARS-CoV-2 positive, as accurately as possible. In hospitals, this is even more important because resources have become scarce. Although polymerase chain reaction (PCR) and point of care (POC) antigen testing capacities have been massively expanded, they are often very time-consuming and cost-intensive and, in some cases, lack appropriate performance. To meet these challenges, we propose the COVIDAL classifier for AI-based diagnosis of symptomatic COVID-19 subjects in hospitals based on laboratory parameters. We evaluate the algorithm's performance by unique multicenter data with approximately 4,000 patients and an extraordinary high ratio of SARS-CoV-2-positive patients. We analyze the influence of data preparation, flexibility in optimization targets, as well as the selection of the test set on the COVIDAL outcome. The algorithm is compared with standard AI, PCR, POC antigen testing and manual classifications of seven physicians by a decision theoretic scoring model including performance metrics, turnaround times and cost. Thereby, we define health care settings in which a certain classifier for COVID-19 diagnosis is to be applied. We find sensitivities, specificities, and accuracies of the COVIDAL algorithm of up to 90 percent. Our scoring model suggests using PCR testing for a focus on performance metrics. For turnaround times, POC antigen testing should be used. If balancing performance, turnaround times, and cost is of interest, as, for example, in the emergency department, COVIDAL is superior based on the scoring model. Collapse

Topics: artificial intelligence mobile application pandemic logistics management data integration

Methods: computational algorithm machine learning normalization data transformation support vector machine artificial neural network

A Collective Opinion Mechanism for Crowdfunding Recommendation

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Jin, Bih-Huang; Li, Yung-Ming; Kamolporn, Sanamlao

Abstract: ... Expand

Abstract: Collapse

Topics: crowdsourcing crowdfunding Twitter social network Python

Methods: sentiment analysis experiment topic model logistic regression parametric test

Improved learning performance for small datasets in high dimensions by new dual-net model for non-linear interpolation virtual sample generation

2023 | Decision Support Systems | Citations: 0

Authors: Lin, Liang-Sian; Lin, Yao-San; Li, Der-Chiang; Liu, Yun-Hsuan

Abstract: The number of reliable samples obtained in early decision-making activity is usu ... Expand

Abstract: The number of reliable samples obtained in early decision-making activity is usually relatively small. Due to variable distribution and incomplete structure of tiny datasets, it is challenging to create reliable and robust predictive modeling using classic statistical and machine learning models in small sample settings. The virtual sample generation (VSG) technique improves model learning accuracies for minimal datasets across diverse applications. Virtual samples on independent variables were generated using established VSG methods predi­ cated on the assumption of a probability distribution or a membership function to fill data gaps. However, in the actual world, non-linear function interactions between variables are common. To address this issue, this paper developed a novel VSG method called Dual-VSG, which generates non-linear interpolation virtual samples using a self-supervised learning (SSL) framework to improve learning performance on small datasets. We generated non-linear interpolation virtual samples without labels by estimating non-linear functions and transforming them into a high-dimensional space using the proposed dual-net model. The weights of the dual-net model are transferred to a downstream task to generate virtual sample labels. To demonstrate the effectiveness of the suggested strategy, this research employed five datasets. On the Backpropagation Neural Networks (BPNN) predictive model, we compared the suggested method’s prediction performance to two state-of-the-art VSG approaches. To assess prediction performance on a regression dataset, the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) are used. Furthermore, the classification accuracy (ACC) and the Fl measure are used to assess classification capability on classification datasets. In addition, the paired t-test was utilized to see if the suggested Dual-VSG approach differed significantly from the other VSG methods in terms of RMSE, MAPE, accuracy (ACC), or F1 score. For short datasets, the suggested Dual-VSG method out­ performs those VSG methods, according to our experimental results. Collapse

Topics: Python programming language

Methods: data transformation t-distributed stochastic neighbor embedding experiment machine learning parametric test

An Innovative Indicator System and Group Decision Framework for Assessing Sustainable Development Enterprises

2021 | Group Decision and Negotiation | Citations: 0

Authors: Deng, Xiang; Cheng, Xiang; Gu, Jing; Xu, Zeshui

Abstract: Empirical research suggests that companies pursuing sustainable development have ... Expand

Abstract: Empirical research suggests that companies pursuing sustainable development have higher credit ratings and lower equity costs. However, a review of the literature shows that no consensus has been reached regarding sustainable development indicators or appropriate decision-making methods for investment in sustainable development enterprises (SDEs). To address this gap, we build a set of sustainable development indicators to guide investors in assessing SDEs, allowing for more informed investment decisions regarding these potential opportunities. Next, to account for the subjectivity of the indicators and the relative weight of investors in group decision-making, we combine a q-rung orthopair fuzzy set with the multiplicative multi-objective optimization by ratio analysis method and introduce an optimization-based consensus model. Finally, we verify the validity of the proposed framework using a practical example. This framework has practical significance for real-world SDE investment and contributes to the literature by introducing a new approach to multi-attribute group decision-making problems. Collapse

Topics: decision making corporate governance fuzzy logic emergency management evaluation criteria

Methods: robustness check normalization data transformation multi objective optimization trade-off analysis method

Theories: fuzzy set theory

Automated Feature Selection for Anomaly Detection in Network Traffic Data

2021 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Nakashima, Makiya; Sim, Alex; Kim, Youngsoo; Kim, Jonghyun; Kim, Jinoh

Abstract: Variable selection (also known as feature selection) is essential to optimize th ... Expand

Abstract: Variable selection (also known as feature selection) is essential to optimize the learning complexity by prioritizing features, particularly for a massive, high-dimensional dataset like network traffic data. In reality, however, it is not an easy task to effectively perform the feature selection despite the availability of the existing selection techniques. From our initial experiments, we observed that the existing selection techniques produce different sets of features even under the same condition (e.g., a static size for the resulted set). In addition, individual selection techniques perform inconsistently, sometimes showing better performance but sometimes worse than others, thereby simply relying on one of them would be risky for building models using the selected features. More critically, it is demanding to automate the selection process, since it requires laborious efforts with intensive analysis by a group of experts otherwise. In this article, we explore challenges in the automated feature selection with the application of network anomaly detection. We first present our ensemble approach that benefits from the existing feature selection techniques by incorporating them, and one of the proposed ensemble techniques based on greedy search works highly consistently showing comparable results to the existing techniques. We also address the problem of when to stop to finalize the feature elimination process and present a set of methods designed to determine the number of features for the reduced feature set. Our experimental results conducted with two recent network datasets show that the identified feature sets by the presented ensemble and stopping methods consistently yield comparable performance with a smaller number of features to conventional selection techniques. Collapse

Topics: information system use IT security IT security defense denial of service attack Python

Methods: experiment anomaly detection computational algorithm machine learning ensemble learning

Theories: set theory

Network Attack Detection using an Unsupervised Machine Learning Algorithm

2020 | HICSS | Citations: 0

Authors: Kumar, Avinash; Glisson, William Bradley; Benton, Ryan

Abstract: With the increase in network connectivity in today's web-enabled environments, t ... Expand

Abstract: With the increase in network connectivity in today's web-enabled environments, there is an escalation in cyber-related crimes. This increase in illicit activity prompts organizations to address network security risk issues by attempting to detect malicious activity. This research investigates the application of a MeanShift algorithm to detect an attack on a network. The algorithm is validated against the KDD 99 dataset and presents an accuracy of 81.2% and detection rate of 79.1%. The contribution of this research is two-fold. First, it provides an initial application of a MeanShift algorithm on a network traffic dataset to detect an attack. Second, it provides the foundation for future research involving the application of MeanShift algorithm in the area of network attack detection. Collapse

Topics: data breach intrusion detection system

Methods: computational algorithm experiment support vector machine cluster analysis machine learning

Assessing the Gap between Technology and the Environmental Sustainability of European Cities

2019 | Information Systems Frontiers | Citations: 6

Authors: Akande, Adeoluwa; Cabral, Pedro; Casteleyn, Sven

Abstract: The growth of cities’ population increased the interest in the opportunities and ... Expand

Abstract: The growth of cities’ population increased the interest in the opportunities and challenges that Information and Communication Technology (ICT) have on carbon footprint reduction, which fosters their environmental sustainability. Using Principal Component Analysis (PCA), six ICT-related variables from European Union (EU) cities were combined into a single two-dimensional ICT index. Then, through cluster analysis, cities were clustered into four groups based on the ICT index and Carbon dioxide (CO2) emissions. Using ICT as an indicator of smartness and CO2 emissions as an indicator of sustainability, we show that it is possible for a city to be smart but not sustainable and vice versa. Results also indicate that there is a gap between cities in northern Europe, which are the top performers in both categories, and cities in south-eastern Europe, which do not perform as well. The need for a common strategy for achieving integrated smart, sustainable and inclusive growth at a European level is demonstrated. Collapse

Topics: systems development environmental sustainability information technology infrastructure climate change smart city

Methods: data transformation principal component analysis cluster analysis hierarchical clustering computational algorithm

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

2019 | International Journal of Information Management | Citations: 0

Authors: Amin, Adnan; Shah, Babar; Khattak, Asad Masood; Lopes Moreira, Fernando Joaquim; Ali, Gohar; Rocha, Alvaro; Anwar, Sajid

Abstract: Cross-Company Churn Prediction (CCCP) is a domain of research where one company ... Expand

Abstract: Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean). Collapse

Topics: information management digital transformation communication service infrastructure

Methods: experiment machine learning data transformation naïve bayes normalization data transformation

Uncovering unobserved heterogeneity bias: Measuring mobile banking system success

2019 | International Journal of Information Management | Citations: 0

Authors: Motiwalla, Luvai F.; Albashrawi, Mousa; Kartal, Hasan B.

Abstract: Mobile banking (MB) involving the use of mobile devices to access bank accounts ... Expand

Abstract: Mobile banking (MB) involving the use of mobile devices to access bank accounts for conducting financial transactions has proliferated in recent years but inconsistently among banking customers. This diversity of use increases the complexity of uncovering unobserved heterogeneity bias in the success of MB systems. This research, first, uses a study of objective measures to cluster 4478 MB users into three homogeneous segments based on the system utilization behaviors captured in the log data. The users were, then, surveyed using a field study of subjective measures based on the information systems (IS) success model. A priori sample segmentation used for facilitating the discovery of unobserved heterogeneity bias in the full sample. The analysis of the subset with 445 users who responded indicated that the path coefficient and explained variances (R2) of the IS model were higher in the segments compared to the full sample; and the influence of success factors on satisfaction and intention to use varied significantly among the segments. Our study, consisting of objective and subjective measures, has theoretical and practical implications for MB usage. It contributes to the IS success model by confirming the existence of unobserved bias in full sample and inconsistent effects of the quality factors on satisfaction and continued usage in the segmented samples. It also assists the banks in identifying MB features that are more appealing to the varying user groups, which could help them with customer retention. Collapse

Topics: information system use usage intention service quality user satisfaction IS success

Methods: cluster analysis survey multivariate analysis of variance partial least squares path modeling hierarchical clustering

Theories: information systems success model unified theory of acceptance and use of technology