Machine Learning Models and Big Data Tools for Evaluating Kidney Acceptance

Ashiku, Lirim; Al-Amin, Md.; Madria, Sanjay; Dağli, Ci̇han H.

doi:10.1016/j.procs.2021.05.019

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…Similarly, Ashiku et al [27] conducted a case study in which they used tools to develop ML models with the aim of evaluating the acceptance of transplanted kidneys. They analyzed these tools mainly based on how much they simplified the development of the corresponding model.…”

Section: Related Workmentioning

confidence: 99%

A Case Study on AI Engineering Practices: Developing an Autonomous Stock Trading System

Grote¹,

Bogner²

2023

Preprint

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Section: Related Workmentioning

confidence: 99%

A Case Study on AI Engineering Practices: Developing an Autonomous Stock Trading System

Grote¹,

Bogner²

2023

Preprint

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“…In the research methods such as data pre-processing algorithms, output ensemble methods, model purposes and seven classes of machine learning models were surveyed to be proposed in future studies. Ashiku et al (2021) developed a specialized analytical method and analyzed integrated technologies with large data to provide support decision making systems in healthcare settings through machine learning. Also, a sentiment analysis on a product was deployed by Onan (2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Cockpit Crew Safety Performance Prediction Based on the Integrated Machine Learning Multi-Class Classification Models and Markov Chain

Borjalilu,

Jolai,

Tavakoli

2023

Aviation

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The main tool of cockpit crew performance evaluation is the recorded flight data used for flight operations safety improvement since all certified airlines require implementation of a safety and quality management system. The safety performance of a flight has been a challenging issue in the aviation industry and plays an important role to acquire competitive benefits. In this study, an integrated multi-class classification machine learning models and Markov chain were developed for cockpit crew performance evaluation during their flights. At the outset, the main features related to a flight are identified based on the literature review, flight operations expert’s statements, and the case study dataset (as numerical example). Afterwards, the flights’ performance is evaluated as a target column based on four multi-class classification models (Decision Tree, Support Vector Machine, Neural Network, and Random Forest). The results showed that the random forest classifier has the greatest value in all evaluation metrics (i.e., accuracy = 0.90, precision = 0.91, recall = 0.97, and F1-score = 0.93). Therefore, this model can be used by the airline companies to predict flight crew performance before the flight in order to prevent or decrease flight safety risks.

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