Ethiopia's first minimally invasive surgery program: A novel approach in global surgical education

Bondzi-Simpson, Adom; Keshishi, Melanie; Ademe, Yonas; Rose, Marci; Tizazu, Ayalew; Uddin, Sameena; Ko, Michael L.

doi:10.1016/j.xjon.2022.11.015

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…Thoracic surgery has risks and benefits for patients in both the short and long term, making it a significant issue in lung cancer patient management [1], [2]. Surgical care affecting the chest, often referred to as the thorax, is the focus of the surgical specialization known as thoracic surgery [3]- [5]. The life expectancy of patients one year after thoracic surgery is one of the issues in thoracic surgery research; therefore, in this study, classification is used to determine whether patients survived or died.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Particle Swarm Optimization Feature Selection on Naïve Bayes for Thoracic Surgery Classification

Shalehah,

Muhammad Itqan Mazdadi,

Andi Farmadi

et al. 2023

j.electron.electromedical.eng.med.inform

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Thoracic surgery is among the operations that are most often performed on patients with lung cancer. Naive Bayes is one of the data mining classification techniques that may be used to handle thoracic surgery data. Therefore, the goal of this study is to assess the precision of all research models using Naive Bayes with and without Particle Swarm Optimization. This study's methodology includes the dataset used, the Naive Bayes algorithm theory, the particle swarm optimization algorithm, test validation using split validation, and performance assessment using the confusion matrix and AUC evaluation approaches. In this inquiry, secondary data are retrieved via the UCI Repository website. Thoracic surgery weight optimization accuracy is increased using particle swarm optimization. The test results of the Naive Bayes technique utilizing the thoracic surgery dataset showed the highest accuracy of 81.91% at a ratio of 80:20 and an AUC value of 0.620. The highest accuracy score is 93.62% with an AUC value of 0.773 at a ratio of 90:10, with three characteristics, namely PRE6, PRE14, and PRE17, having zero weight. This accuracy score was achieved when Particle Swarm Optimization was used to refine feature selection for attribute weighting. As a consequence, Naïve Bayes accuracy in thoracic surgery has increased as a result of attribute weighting on feature selection utilizing Particle Swarm Optimization. In turn, this research contributes to increasing the precision and efficiency with which thoracic surgical data are processed, which benefits lung cancer diagnosis in both speed and accuracy.

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