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Cancer Prediction Using Graph Database

Cancer Prediction Using Graph Database

Ansh Gulati, Ameya Taneja, Saurabh Rawat, Anushree Sah
Copyright: © 2024 |Pages: 11
ISBN13: 9798369352717|ISBN13 Softcover: 9798369352724|EISBN13: 9798369352731
DOI: 10.4018/979-8-3693-5271-7.ch005
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MLA

Gulati, Ansh, et al. "Cancer Prediction Using Graph Database." Machine Learning Techniques and Industry Applications, edited by Pramod Kumar Srivastava and Ashok Kumar Yadav, IGI Global, 2024, pp. 90-100. https://doi.org/10.4018/979-8-3693-5271-7.ch005

APA

Gulati, A., Taneja, A., Rawat, S., & Sah, A. (2024). Cancer Prediction Using Graph Database. In P. Srivastava & A. Yadav (Eds.), Machine Learning Techniques and Industry Applications (pp. 90-100). IGI Global. https://doi.org/10.4018/979-8-3693-5271-7.ch005

Chicago

Gulati, Ansh, et al. "Cancer Prediction Using Graph Database." In Machine Learning Techniques and Industry Applications, edited by Pramod Kumar Srivastava and Ashok Kumar Yadav, 90-100. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-5271-7.ch005

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Abstract

This research chapter aims to provide a comprehensive overview of cancer cases and rates in the various states of the United States. It explores the trends and patterns of cancer incidence and mortality in the country, as well as the factors such as age, sex/gender, type of cancer whether it is lung or breast cancer and its rates and also the factors that contribute to the development and progression of the disease. The chapter reviews the latest statistics on cancer rates mainly breast and lung cancer in different population groups, including age, sex/gender, and geographical location/different states of USA. By analyzing the data, the project aims to provide insights and predictions related to the occurrence of cancer in the US. The Python code implements visualizations of cancer data for various states in the USA using Pandas and Matplotlib libraries. The dataset is read into a Pandas data frame and various types of visualizations are produced for the cancer data, including scatter plots, and bar graphs. The scatter plot represents the rate of lung and breast cancer in various states of the USA, and the bar graphs represent the total number of breast cancer and lung cancer, as well as the cancer rates in people of different age groups for each state. The visualizations allow for the comparison of cancer rates and total numbers between different states and age groups, aiding in identifying the states with higher cancer rates and potentially identifying any trends or patterns. The chapter concludes by discussing the challenges and opportunities for cancer prevention, early detection, and treatment in the United States, and the implications for public health policy and practice. Potential applications of this analysis include informing strategies for cancer prevention and treatment in different states and age groups. The project could have implications for public health and policy, as well as for advancing the understanding of cancer and its impact on society. Overall, this chapter aims to provide a comprehensive and up-to-date picture of the burden of cancer in the United States and to identify areas for further research and action.

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