Treffer: A Meta-ensemble Predictive Model for the Risk of Lung Cancer.

The lungs play a vital role in supplying oxygen to every cell, filtering air to prevent harmful substances, and supporting defense mechanisms. However, they remain susceptible to the risk of diseases such as infections, inflammation, and cancer that affect the lungs. Meta-ensemble techniques are prominent methods used in machine learning to enhance the accuracy of classifier learning systems in making predictions. This work proposes a robust predictive model using a meta-ensemble method to identify high-risk individuals with lung cancer, thereby taking early action to prevent longterm problems benchmarked upon the Kaggle Machine Learning practitioners' Lung Cancer Dataset. Three machine learning ensemble modelsdRandom Forest, Adaptive Boosting (AdaBoost), and Gradient Boostingdwere used to develop the meta-ensemble models proposed in this paper, whereby the three ensemble models were adopted as base classifiers while one of them was adopted as the meta-classifier. In addition, two of the ensemble models were used as base classifiers, while the third was used as a meta-classifier to evaluate lung cancer risk prediction. Different graphs were evaluated to show that people with these features are liable to develop lung cancer. The proposed model has immensely improved prediction performance. The meta-ensemble models were simulated using the Python simulation environment, and the 5-fold cross-validation technique was used. The model validation was carried out using several known performance evaluation methodologies. The results of the experiments showed that gradient boosting achieved a maximum accuracy of 100%, an area under the curve (AUC), and a precision of 100%. The proposed model was compared with novel machine learning methods and popular state-of-the-art (SOTA) deep learning techniques. It was confirmed from the results that the model in this study had the best accuracy at lung cancer risk prediction. This study's results can be utilized to enhance the performance of actual patient risk prediction systems in the future. [ABSTRACT FROM AUTHOR]

المقال يناقش تطوير نموذج تنبؤي ميتا-إنسيمل لتقييم مخاطر سرطان الرئة من خلال تقنيات التعلم الآلي. استخدم الباحثون ثلاثة نماذج إنسيمل—الغابة العشوائية (Random Forest)، التعزيز التكيفي (AdaBoost)، والتعزيز التدرجي (Gradient Boosting)—محققين دقة قصوى تصل إلى 100% في تحديد الأفراد ذوي المخاطر العالية باستخدام مجموعة بيانات من كاجل (Kaggle). تؤكد الدراسة على أهمية الكشف المبكر وإمكانية دمج هذا النموذج في أنظمة الرعاية الصحية لتحسين توقع مخاطر المرضى. كما تستعرض الدراسة منهجيات مختلفة وتطورات في توقع سرطان الرئة، مع تسليط الضوء على دور اختيار الميزات والتعلم الإنسيمل في تعزيز الأداء التنبؤي عبر مجالات مختلفة. [Extracted from the article]

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