A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science

Alloghani, Mohamed; Al‐Jumeily, Dhiya; Mustafina, Jamila; Hussain, Abir; Aljaaf, Ahmed J.

doi:10.1007/978-3-030-22475-2_1

Cited by 340 publications

(169 citation statements)

References 86 publications

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“…Methods utilizing such data are known as semi-supervised learning methods. A systematic review of supervised and unsupervised learning techniques can be found in [26].…”

Section: A ML In Healthcare: the Big Picturementioning

confidence: 99%

Secure and Robust Machine Learning for Healthcare: A Survey

Qayyum

Qadir

Bilal

et al. 2021

IEEE Rev. Biomed. Eng.

308

144

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Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from onedimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.

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“…Methods utilizing such data are known as semi-supervised learning methods. A systematic review of supervised and unsupervised learning techniques can be found in [26].…”

Section: A ML In Healthcare: the Big Picturementioning

confidence: 99%

Secure and Robust Machine Learning for Healthcare: A Survey

Qayyum

Qadir

Bilal

et al. 2021

IEEE Rev. Biomed. Eng.

308

144

View full text Add to dashboard Cite

show abstract

“…Results of the Study Alloghani et al (2020) The main aim of the methodical revision was analysing the research papers which had been published differently during the years 2015 to 2018. It was done to achieve the perfect application for the ML strategies related to finding solutions for many issues in the models.…”

Section: Aim Of the Studymentioning

confidence: 99%

The Role of Big Data and Machine Learning in COVID-19

Ababneh¹,

Aljarrah²,

Karagozlu³

2020

BRAIN

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The big rise in the existence of digital data contributed to creating many good chances, especially related to corporations, institutions and firms. Also, it gives the capability to scrimp data regarding its major or area, where the countries have benefited from the analysis of big data (BD) greatly in the face of epidemics and diseases, especially COVID-19 since BD is now available everywhere around us, from official reports and scientific studies related to virology and epidemiology. The general aim of this study is to clarify how the conjunction among both BD and machine learning (ML) created huge differences in data science and a big influence on the applications related to a lot of fields chiefly in COVID-19. The method which is used in this study 'relevance tree' by identifying papers related to ML and BD, especially in COVID-19. The results have been shown that the use of reinforcement learning in analyzing BD provides effective and tremendous results, although it faces many challenges and restrictions that have been explained in detail in this study. In addition, the results showed that most of the countries in the time of Corona turned into smart cities, totally dependent on smart applications based on the analysis of BD using ML, and one of the most important applications that were circulated around the world global positioning system. In addition to the results that have been found, data privacy is one of the most important challenges facing data analysis. Consequently, it recommended future researchers to focus on studying the challenges faced by ML in analyzing medical data in the COVID-19 era.

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“…Following are the most popular supervised algorithms: logistic regression, decision trees (DTs), random forest (RF), extreme gradient boosting, support vector machines (SVMs), Naïve Bayes, adaptive boosting (AdaBoost), arti cial neural network (ANN) etc. [31].…”

Section: 4a Supervised Learning Algorithmsmentioning

confidence: 99%

“…These algorithms are highly popular in the tasks to discover the natural clusters, dimension reduction, anomaly detection, etc. k-Means clustering, principal component analysis (PCA), factor analysis (FA), singular value decomposition (SVD), apriori algorithm (association rule) are some popular examples of unsupervised learning algorithms [31].…”

Section: 4b Unsupervised Learning Algorithmsmentioning

confidence: 99%

Predicting Endometriosis Onset Using Machine Learning Algorithms

Kleczyk¹,

Peri²,

Yadav³

et al. 2020

Preprint

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Background Endometriosis is a common progressive female health disorder in which tissues similar to the lining of the uterus grow on other parts of the body like ovaries, fallopian tubes, bowel, and other parts of reproductive organs. In women, it is one of the most common causes of pelvic pain and infertility. In the US, one in every ten women of reproductive age group has endometriosis. The actual cause of endometriosis is still unknown, and it is quite difficult to diagnose. There are several theories regarding the cause; however, not a single theory has been scientifically proven. Methods In this paper, we try to identify the drivers of endometriosis’ diagnoses via leveraging advanced Machine Learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a great extent, if likelihood of endometriosis can be predicted well in advance. As a result, the proper medical care and treatment can be given to the impacted patients. To demonstrate the feasibility, Logistic Regression (LR) and eXtreme Gradient Boosting (XGB) algorithms were trained on 36 months of medical history data. Results The machine learning models were used to predict the likelihood of disease on qualified patients from the healthcare claims patient level database. Several directly and indirectly features were identified as important in accurate prediction of the condition onset, including selected diagnosis and procedure codes. Conclusions Leveraging the machine learning approaches can aid early prediction of the disease and offer an opportunity for patients to receive the needed medical treatment earlier in the patient journey. Creating a typing tool that can be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers could further aid the objective of improving the diagnosis activities and inform the diagnostic processes that would result in timely and precise diagnosis, ultimately increasing patient care and quality of life.

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A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science

Cited by 340 publications

References 86 publications

Secure and Robust Machine Learning for Healthcare: A Survey

Secure and Robust Machine Learning for Healthcare: A Survey

The Role of Big Data and Machine Learning in COVID-19

Predicting Endometriosis Onset Using Machine Learning Algorithms

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