Lung Cancer Incidence Prediction Using Machine Learning Algorithms

Tuncal, Kubra; Şekeroğlu, Boran; Ozkan, Cagri

doi:10.12720/jait.11.2.91-96

Cited by 39 publications

(11 citation statements)

References 22 publications

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“…A regional modeling approach is recommended if the local features and data are available. from the training data (support vectors); and tries to minimize the distance between the observed data and predicted data in order to improve the performance [15]. In this study, the support vector machine with a radial basis function (RBF) kernel was The cost parameter and the RBF kernel parameter sigma were tuned in this model.…”

Section: Limitationmentioning

confidence: 99%

“…They found that LR and SVR outperformed the other models with R-squared values of 0.99 and 0.98, respectively. Tuncal et al [ 15 ], proposed several machine learning algorithms, including SVR, backpropagation NN, and long short-term memory NN, to provide an effective and rapid prediction of lung cancer incidence. The results show that SVR gives better results than the other considered algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…The function of SVR is to discover a function as a hyperplane (separation line) in the form of a regression function that fits all input data with an error and makes it as thin as possible [ 72 ]. SVR minimizes the error by maximizing the margin of the hyperplane, it creates subclasses from the training data (support vectors); and tries to minimize the distance between the observed data and predicted data in order to improve the performance [ 15 ]. In this study, the support vector machine with a radial basis function (RBF) kernel was used.…”

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confidence: 99%

“…The balance between interpretive ability and predictive power offered by the cubist model motivated us to consider this model for this study. The cubist model was shown to be more promising in terms of deciphering complicated interactions between variables and is superior in terms of execution time [ 15 , 82 ]. In this model, a tree is grown, where the branches can be thought of as an “if-then” set of rules, and the terminal leaves contain predictive linear regression models.…”

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confidence: 99%

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How Is the Lung Cancer Incidence Rate Associated with Environmental Risks? Machine-Learning-Based Modeling and Benchmarking

Wang

Chen

Hernanda

et al. 2022

IJERPH

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The lung cancer threat has become a critical issue for public health. Research has been devoted to its clinical study but only a few studies have addressed the issue from a holistic perspective that included social, economic, and environmental dimensions. Therefore, in this study, risk factors or features, such as air pollution, tobacco use, socioeconomic status, employment status, marital status, and environment, were comprehensively considered when constructing a predictive model. These risk factors were analyzed and selected using stepwise regression and the variance inflation factor to eliminate the possibility of multicollinearity. To build efficient and informative prediction models of lung cancer incidence rates, several machine learning algorithms with cross-validation were adopted, namely, linear regression, support vector regression, random forest, K-nearest neighbor, and cubist model tree. A case study in Taiwan showed that the cubist model tree with feature selection was the best model with an RMSE of 3.310 and an R-squared of 0.960. Through these predictive models, we also found that apart from smoking, the average NO2 concentration, employment percentage, and number of factories were also important factors that had significant impacts on the incidence of lung cancer. In addition, the random forest model without feature selection and with feature selection could support the interpretation of the most contributing variables. The predictive model proposed in the present study can help to precisely analyze and estimate lung cancer incidence rates so that effective preventative measures can be developed. Furthermore, the risk factors involved in the predictive model can help with the future analysis of lung cancer incidence rates from a holistic perspective.

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Section: Limitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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How Is the Lung Cancer Incidence Rate Associated with Environmental Risks? Machine-Learning-Based Modeling and Benchmarking

Wang

Chen

Hernanda

et al. 2022

IJERPH

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“…Some efforts have been made in the literature to use machine learning and reinforcement learning techniques to fight against COVID-19 spread [1][2][3][4]. These models have been proven effective in a series of forecasting problems [5][6][7][8][9][10]. Among previous forecasting research, several studies using reinforcement learning model had employed public data to predict the COVID-19 pandemic in other area, such as India and China, at early stage of this worldwide pandemic.…”

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confidence: 99%

COVID-19 Vaccine Distribution Policy Design with Reinforcement Learning

Tan

2021

2021 5th International Conference on Advances in Image Processing (ICAIP)

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COVID-19 has become a global crisis and the vaccine has been seen as an effective approach to stop the epidemic spread. However, the resources for distributing and allocating different types of vaccines are limited and we need a better vaccine distribution policy design to prevent the spread of COVID-19 more efficiently. In this study, a pipeline of combing a random forest model and a DQN model is proposed. The random forest model is built to predict the daily new confirmed cases with the vaccine data as the inputs. And the DQN model is built to design the daily allocation ratio of three types of vaccines, with the aim to minimize the new confirmed cases. The experimental results based on the real-world datasets collected in San Diego validate the effectiveness of the proposed pipeline.

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An Efficient Classification Method Using GLCM and Decision Tree Classifier

Gulande,

Awale

2023

Lecture Notes in Networks and Systems

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Lung Cancer Incidence Prediction Using Machine Learning Algorithms

Cited by 39 publications

References 22 publications

How Is the Lung Cancer Incidence Rate Associated with Environmental Risks? Machine-Learning-Based Modeling and Benchmarking

How Is the Lung Cancer Incidence Rate Associated with Environmental Risks? Machine-Learning-Based Modeling and Benchmarking

COVID-19 Vaccine Distribution Policy Design with Reinforcement Learning

An Efficient Classification Method Using GLCM and Decision Tree Classifier

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