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Lung Cancer Dataset Lung cancer dataset without attribute definitions 56 features are given for each case 32 Text Classification 1992 [270] [271] Z. Hong et al. Arrhythmia Dataset Data for a group of patients, of which some have cardiac arrhythmia. 276 features for each instance. 452 Text Classification 1998 [272] [273] H. Altay et al.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Lung cancer is the most diagnosed and deadliest cancer worldwide, with 2.2 million cases in 2020 resulting in 1.8 million deaths. [3] Lung cancer is rare in those younger than 40; the average age at diagnosis is 70 years, and the average age at death 72. [2] Incidence and outcomes vary widely across the world, depending on patterns of tobacco use.
The Original Cohort, founded in 1948, consisted of 5,209 men and women. Requirements for entry were an age between 30 and 62 years at the time of first examination, with no history of heart attack or stroke. Due to lukewarm interest at first, doctors, nurses and healthcare workers volunteered for the study to set an example for patients.
Neural networks are typically trained through empirical risk minimization.This method is based on the idea of optimizing the network's parameters to minimize the difference, or empirical risk, between the predicted output and the actual target values in a given dataset. [4]