Blood Diseases Detection using Classical Machine Learning Algorithms

Alsheref, Fahad Kamal; Hassan, Wael M.

doi:10.14569/ijacsa.2019.0100712

Cited by 34 publications

(16 citation statements)

References 16 publications

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“…Supervised learning means the algorithm learns to predict data from input data, and this type has input and output data. While in unsupervised learning, the algorithm has only input data and learns the inherent structure from the input data [21]. Moreover, semi-supervised learning depends on both techniques (supervised and unsupervised).…”

Section: Machine Learning Typesmentioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

176

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Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

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Section: Machine Learning Typesmentioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

176

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“…Combining blood tests with advanced AI-based methods can significantly improve the sensitivity and accuracy of diagnosis [ 105 , 106 , 107 ]. In the recent past, several studies have been published which show the applicability of these techniques in predicting common diseases [ 107 , 108 ]. Alsheref et al assessed various ML algorithms to detect blood diseases.…”

Section: Role Of Ai In the Screening Of Covid-19 Infected Patients And Diagnosismentioning

confidence: 99%

“…Alsheref et al assessed various ML algorithms to detect blood diseases. In this study, the author assessed the predictability of commonly used supervised algorithms to detect blood diseases, and they achieved ~98% accuracy to predict the occurrence of blood disease with LogitBoost algorithms [ 108 ]. Park et al built three models, LightGBM and extreme gradient boosting (XGBoost) ML models and a DNN (deep neural network) based model on 5145 cases and 326686 laboratory tests [ 109 ].…”

Section: Role Of Ai In the Screening Of Covid-19 Infected Patients And Diagnosismentioning

confidence: 99%

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

et al. 2021

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As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning(ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

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“…In [ 1 ], the authors use laboratory data on patients also to detect blood diseases. In their approach, they select several candidate models within minimal pre-treatment of data to understand which algorithm behaves better.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting special care during the COVID-19 pandemic: a machine learning approach

Bezzan

Rocco

2021

Health Inf Sci Syst

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More than ever, COVID-19 is putting pressure on health systems worldwide, especially in Brazil. In this study, we propose a method based on statistics and machine learning that uses blood lab exam data from patients to predict whether patients will require special care (hospitalization in regular or special-care units). We also predict the number of days the patients will stay under such care. The two-step procedure developed uses Bayesian Optimisation to select the best model among several candidates. This leads us to final models that achieve 0.94 area under ROC curve performance for the first target and 1.87 root mean squared error for the second target (which is a 77% improvement over the mean baseline)—making our model ready to be deployed as a decision system that could be available for everyone interested. The analytical approach can be used in other diseases and can help to plan hospital resources in other contexts.

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Blood Diseases Detection using Classical Machine Learning Algorithms

Cited by 34 publications

References 16 publications

The Role of Machine Learning Algorithms for Diagnosing Diseases

The Role of Machine Learning Algorithms for Diagnosing Diseases

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

Predicting special care during the COVID-19 pandemic: a machine learning approach

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