Life expectancy and cancer survival in Oncosalud: outcomes over a 15-year period in a Peruvian private institution

Life expectancy is a fundamental measure of social progress, with excellent data enabling us to measure progress. Britain made huge strides in improving health and life expectancy during the second half of the twentieth century, life expectancy increasing by over ten years. There were large reductions in infant mortality and control of infectious diseases, as well as a decline in smoking and its related causes of death. Progress continued into the twenty-first century, although progress in increasing disability-free life expectancy among women stalled, and social class inequalities in infant mortality, after narrowing considerably, also stalled. Moreover, peer countries such as France, Germany, and Italy made even more progress than Britain in extending life expectancy and reducing infant mortality. New challenges such as obesity appear likely to hinder Britain’s progress in the future. Cancer survival rates in Britain, although improving, remain considerably lower than in peer countries.

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Post Thoracic Surgery Life Expectancy Prediction Using Machine Learning

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.20211001.oa32 ◽

2021 ◽

Vol 16 (4) ◽

pp. 1-20

Author(s):

Akshaya Ravichandran ◽

Krutika Mahulikar ◽

Shreya Agarwal ◽

Suresh Sankaranarayanan

Keyword(s):

Neural Network ◽

Machine Learning ◽

Lung Cancer ◽

Life Expectancy ◽

Thoracic Surgery ◽

Deep Neural Network ◽

Cancer Survival ◽

Small Cell ◽

Machine Learning Algorithms ◽

Post Surgery

Lung cancer survival rate is very limited post-surgery irrespective it is “small cell and non-small cell”. Lot of work have been carried out by employing machine learning in life expectancy prediction post thoracic surgery for patients with lung cancer. Many machine learning models like Multi-layer perceptron (MLP), SVM, Naïve Bayes, Decision Tree, Random forest, Logistic regression been applied for post thoracic surgery life expectancy prediction based on data sets from UCI. Also, work has been carried out towards attribute ranking and selection in performing better in improving prediction accuracy with machine learning algorithms So accordingly, we here have developed Deep Neural Network based approach in prediction of post thoracic Life expectancy which is the most advanced form of Neural Networks . This is based on dataset obtained from Wroclaw Thoracic Surgery Centre machine learning repository which contained 470 instances On comparing the accuracy, the results indicate that the deep neural network can be efficiently used for predicting the life expectancy.

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The Impact of Socioeconomic Factors on Cancer Survival Rate

Basic & Clinical Cancer Research ◽

10.18502/bccr.v12i1.5728 ◽

2021 ◽

Author(s):

Rajabali Daroudi ◽

Nasrin Sargazi ◽

Arya Sakhidel Hovasin ◽

Mohammadreza Sheikhy-Chaman

Keyword(s):

Prostate Cancer ◽

Cervical Cancer ◽

Survival Rate ◽

Life Expectancy ◽

Socioeconomic Factors ◽

Cancer Survival ◽

Healthcare Expenditure ◽

Survival Rates ◽

Literacy Rate ◽

The Impact

Background: Socioeconomic status, as a major determinant of health, has a considerable impact on the cancer survival rate. The present study aimed to investigate the impact of socioeconomic factors on the 5-year survival rate for the most common cancer types in 56 countries. Methods: In this ecological study, 5-year survival data for gastric cancer, colon cancer, lung cancer, breast cancer, cervical cancer, ovarian cancer, prostate cancer, and leukemia during the period of 2005-2009 and socioeconomic factors including gross domestic product (GDP), life expectancy, literacy rate, urbanization and healthcare expenditure were extracted from the CONCORD-2 study and the World Bank database, respectively. multivariate regression analysis was used to estimate the model with the ordinary least-squares (OLS) method using Stata 14 software. Results: The GDP coefficient for breast cancer, cervical cancer, and leukemia was positive and significant. No correlation was identified between gastric, colon, lung, ovarian, and prostate cancer and GDP. Gastric, colon, breast, and prostate cancers had a positive and significant correlation with life expectancy. In contrast, no significant correlation was found between lung cancer, cervical cancer, ovarian cancer, leukemia and life expectancy. There was no correlation between cancer survival rate and literacy rate, or urbanization. There was only a positive correlation between prostate cancer and healthcare expenditure. Furthermore, there was no statistically significant relationship between gastric and ovarian cancers and socioeconomic variables. Finally, GDP and life expectancy had the most significant impact on cancer survival rates. Conclusion: Different countries can play a key role in increasing cancer survival rates by implementing policies to improve economic and social factors.

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