Multi-Criteria Decision Support System for Lung Cancer Prediction

Al-Bander, Baidaa; Fadil, Yousra Ahmed; Mahdi, Hussain Falih

doi:10.1088/1757-899x/1076/1/012036

Cited by 7 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lung cancer, recognized by the World Health Organization (WHO) as the leading cause of cancer-related deaths, remains a significant global health challenge. 41 – 50 Early detection can critically enhance survival rates and improve patient outcomes. To address this, our study investigated the potential of ML for the early diagnosis of lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Optimizing lung cancer classification through hyperparameter tuning

Nabeel,

Bazai,

Alasbali

et al. 2024

DIGITAL HEALTH

View full text Add to dashboard Cite

Artificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate. Two primary factors contribute to its onset: genetic predisposition and environmental factors, such as smoking and exposure to pollutants. Recognizing the need for more effective diagnosis techniques, our study embarked on devising a machine learning strategy tailored to boost precision in lung cancer detection. Our aim was to devise a diagnostic method that is both less invasive and cost-effective. To this end, we proposed four methods, benchmarking them against prevalent techniques using a universally recognized dataset from Kaggle. Among our methods, one emerged as particularly promising, outperforming the competition in accuracy, precision and sensitivity. This method utilized hyperparameter tuning, focusing on the Gamma and C parameters, which were set at a value of 10. These parameters influence kernel width and regularization strength, respectively. As a result, we achieved an accuracy of 99.16%, a precision of 98% and a sensitivity rate of 100%. In conclusion, our enhanced prediction mechanism has proven to surpass traditional and contemporary strategies in lung cancer detection.

show abstract

Section: Discussionmentioning

confidence: 99%

Optimizing lung cancer classification through hyperparameter tuning

Nabeel,

Bazai,

Alasbali

et al. 2024

DIGITAL HEALTH

View full text Add to dashboard Cite

show abstract

“…The decision goal defined for the criteria in AHP is represented as a hierarchy in problem modeling [ 33 ]. The decision hierarchy is divided into four levels: Level 1: the decision problem aim (at the top); Level 2: the criteria; Level 3: subcriteria; and Level 4: the set of alternatives.…”

Section: Methodsmentioning

confidence: 99%

Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

Deif

Solyman

Alsharif

et al. 2021

Sensors

View full text Add to dashboard Cite

The sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients’ priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions’ priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley’s Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID-19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians’ decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies.

show abstract

“…To build FAHP, the fuzzy theory is incorporated into the fundamental AHP. The AHP approach, for example, has been used in the past to forecast sickness [36]. As a result, FAHP is used to predict the occurrence of diabetes in this study.…”

Section: Fuzzy Analytical Hierarchy Process (Fahp)mentioning

confidence: 99%

Soft computing techniques for early diabetes prediction

Abdulkareem

Radhi²,

Fadil³

et al. 2022

IJEECS

Self Cite

View full text Add to dashboard Cite

Diabetes mellitus is a chronic, life-threatening, and complicated condition. Around 1.5 million deaths due to diabetes have been documented, according to a World Health Organization (WHO) estimation in 2019. In the world of medicine, predicting diabetes risk is a difficult and time-consuming task. Many past studies have been conducted to investigate and clarify diabetes symptoms and variables. To solve these persisting issues, however, more critical clinical criteria must be considered. A comparative analysis based on three soft computing strategies for diabetes prediction has been carried out and achieved in this work. Among the computational intelligence methods used in this study are fuzzy analytical hierarchy processes (FAHP), support vector machine (SVM), and artificial neural networks (ANNs). The techniques reveal promising performance in predicting diabetes reliably and effectively in terms of several classification evaluation metrics, according to experimental analysis and assessment conducted on 520 participants using a publicly available dataset.

show abstract

Multi-Criteria Decision Support System for Lung Cancer Prediction

Cited by 7 publications

References 24 publications

Optimizing lung cancer classification through hyperparameter tuning

Optimizing lung cancer classification through hyperparameter tuning

Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

Soft computing techniques for early diabetes prediction

Contact Info

Product

Resources

About