Machine Learning-as-a-Service and Its Application to Medical Informatics

Tafti, Ahmad P.; LaRose, Eric; Badger, Jonathan C.; Kleiman, Ross; Peissig, Peggy L.

doi:10.1007/978-3-319-62416-7_15

Cited by 18 publications

(9 citation statements)

References 21 publications

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“…The objective of the proposed work is to analyse the performance of various algorithms and investigate their training time, prediction time, attack detection rate and false alarm rate by considering network instances of UNSW NB-15 dataset on a sophisticated Machine learning as a service (MLaaS) platform called Microsoft Azure Machine Learning Studio(MAMLS).A modern and a comprehensive dataset is essential to evaluate the effectiveness of the proposed approach and UNSW NB-15 dataset serves the purpose [17][18][19]. A significant advantage of any MLaaS offering is its ability to save computational resources that involve exceesive costs [20,21].The novelty of the proposed approach is that the false alarm rate generated by two class decision forest model is quite negligible and the attack detection capability of multiclass decision forest model is definitely desirable. It is worthwhile to mention that the results of classification tasks are quite superior than existing state of the art techniques.Some existing studies in the literature have explored the performance of different machine learning algorithms on UNSW NB-15 dataset as elucidated below.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of binary and multiclass models using azure machine learning

Rajagopal

Hareesha

Kundapur

2020

IJECE

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Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms emphasize more on improving attack detection rate and reducing false alarms but time efficiency is often overlooked. Therefore, in order to address this limitation, a modern solution has been presented using Machine Learning-as-a-Service platform. The proposed work analyses the performance of eight two-class and three multiclass algorithms using UNSW NB-15, a modern intrusion detection dataset. 82,332 testing samples were considered to evaluate the performance of algorithms. The proposed two class decision forest model exhibited 99.2% accuracy and took 6 seconds to learn 1,75,341 network instances. Multiclass classification task was also undertaken wherein attack types like generic, exploits, shellcode and worms were classified with a recall percentage of 99%, 94.49%, 91.79% and 90.9% respectively by the multiclass decision forest model that also leapfrogged others in terms of training and execution time.

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Section: Introductionmentioning

confidence: 99%

Performance analysis of binary and multiclass models using azure machine learning

Rajagopal

Hareesha

Kundapur

2020

IJECE

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“…Other papers focused on how to speed up the processing and consumption of patient data and analytics, an endeavor that is necessary in both primary and in secondary use settings. In this respect, Tafti et al 12 investigated the accuracy, performance, and efficiency of BigML and Algorithmica machine learning as a service environment with four datasets from the Surveillance, Epidemiology, and End Results (SEER) repository and two datasets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository. It was shown that these systems have various capabilities and costs, but that there remain concerns over the security of the system and the privacy of data during the computation.…”

Section: Discussionmentioning

confidence: 99%

Between Access and Privacy: Challenges in Sharing Health Data

Malin

Goodman

2018

Yearb Med Inform

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“…In addition to cloud-based bioinformatics platforms, machine learning-as-a-service is being offered by leading commercial cloud service providers like Amazon, Google, Microsoft and IBM. ML-as-a-service makes implementation of complex ML algorithms on large-scale datasets convenient for biomedical researchers [ 222 ]. It is apparent that the future of multi-omics integrative analysis is reliant on ML algorithms, and cloud-based solutions provide feasible options to implement them at large-scale.…”

Section: Big Data Scalabilitymentioning

confidence: 99%

Machine Learning and Integrative Analysis of Biomedical Big Data

Mirza

Wang

et al. 2019

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256

165

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Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

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Machine Learning-as-a-Service and Its Application to Medical Informatics

Cited by 18 publications

References 21 publications

Performance analysis of binary and multiclass models using azure machine learning

Performance analysis of binary and multiclass models using azure machine learning

Between Access and Privacy: Challenges in Sharing Health Data

Machine Learning and Integrative Analysis of Biomedical Big Data

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