Enhancing the Efficiency of Lung Disease Prediction using CatBoost and Expectation Maximization Algorithms

Sudharson, D; Retheneka, S Oviya; Yogeswari, L; Logita, S J; Shankari, S. Uma; Surraya, S M Noor

doi:10.1109/icirca54612.2022.9985761

Cited by 8 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(c) CatBoost CatBoost employs a highly effective strategy that mitigates overfitting, enabling the utilization of the entire dataset for training purposes. The application of Cat-Boost extends notably to the health domain, where it excels in predictive modeling for various diseases, including brain, asthma, prostate, and breast cancer [46][47][48]. Its impressive performance and versatility in healthcare settings make Cat-Boost a valuable tool for accurate disease prediction and improved patient outcomes.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…The model's performance was evaluated using a confusion matrix and a comprehensive set of metrics, including True Positives, True Negatives, False Positives, False Negatives, precision, recall, F1-score, accuracy, and other relevant measures. The rationale behind employing this evaluation methodology is its ability to provide a detailed and rigorous assessment of the model's predictive capabilities in many literature reviews [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53]. It offers insights into how well the model performs across different classification aspects.…”

Section: Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 Detection

Uddin,

Prity,

Tasnim

et al. 2023

Hum-Cent Intell Syst

View full text Add to dashboard Cite

The COVID-19 pandemic has unleashed an unprecedented global crisis, releasing a wave of illness, mortality, and economic disarray of unparalleled proportions. Numerous societal and behavioral aspects have conspired to fuel the rampant spread of COVID-19 across the globe. These factors encompass densely populated areas, adherence to mask-wearing protocols, inadequate awareness levels, and various behavioral and social practices. Despite the extensive research surrounding COVID-19 detection, an unfortunate dearth of studies has emerged to meticulously evaluate the intricate interplay between socio-demographic and behavioral factors and the likelihood of COVID-19 infection. Thus, a comprehensive online-based cross-sectional survey was methodically orchestrated, amassing data from a substantial sample size of 500 respondents. The precisely designed survey questionnaire encompassed various variables encompassing socio-demographics, behaviors, and social factors. The Bivariate Pearson’s Chi-square association test was deftly employed to unravel the complex associations between the explanatory variables and COVID-19 infection. The feature importance approach was also introduced to discern the utmost critical features underpinning this infectious predicament. Four distinct Machine Learning (ML) algorithms, specifically Decision Tree, Random Forest, CatBoost, and XGBoost, were employed to accurately predict COVID-19 infection based on a comprehensive analysis of socio-demographic and behavioral factors. The performance of these models was rigorously assessed using a range of evaluation metrics, including accuracy, recall, precision, ROC-AUC score, and F1 score. Pearson’s Chi-square test revealed a statistically significant association between vaccination status and COVID-19 infection. The use of sanitizer and masks, the timing of infection, and the interval between the first and second vaccine doses were significantly correlated with the likelihood of contracting the COVID-19 virus. Among the ML models tested, the XGBoost classifier demonstrated the highest classification accuracy, achieving an impressive 97.6%. These findings provide valuable insights for individuals, communities, and policymakers to implement targeted strategies aimed at mitigating the impact of the COVID-19 pandemic.

show abstract

Section: Machine Learning Techniquesmentioning

confidence: 99%