Özet:

Abstract Lung cancer affects people of all ages and is caused by cell proliferation in the lungs uncontrollably. This leads to extreme respiratory issues both in the inhalation and exhalation of the chest. Cigarette smoking and passive smoking are the major causes of lung cancer, according to the World Health Organization. Unlike other malignancies, lung cancer mortality rates increase every day in both young and elderly persons. The death rate remains unacceptably high, despite the availability of high-tech medical installations for accurate diagnosis and successful medical treatment. Consequently, it is vital to take early steps to recognize signs and consequences early so that a more exact diagnosis may be achieved. Due to its great computational ability to forecast early disease with reliable data processing, machine learning had significant impact in recent years on the healthcare area. Numerous methods for classification of lung cancer data into benign and malignant categories are analyzed in the UCI machine learning repository to tackle existing challenges. In this paper a Labelled Priority based Weighted Classifier for Feature Extraction for Accurate Lung Tumour Detection (LPbWCFELTD) using Machine Learning Model is proposed that accurately identifies the lung tumour by considering the relevant features from the dataset. The new model is compared to established models and the findings demonstrate that the precise levels of the proposed model are better for detecting lung tumours.

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