Browsing by Author "Janani, Selvaraj"

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EVALUATION OF AGE-STANDARDIZED CANCER BURDEN IN WESTERN TAMIL NADU, INDIA
(Indian Journal of Community Health, 2014-09-20) Janani, Selvaraj; Prashanthi Devi, Marimuthu; Harathi, Parasur Babu
The burden of cancer is growing globally and is one of the top leading causes of death. Information on cancer patterns is essential for effective planning of cancer control interventions. Aims and Objectives:The present cross sectional study aims to explore the patterns and trends of the cancer incidences in the western regions of Tamil Nadu, India including Coimbatore, Erode, Tiruppur,Salem, Namakkal and Nilgiris. Materials andMethods:A sum of 14392 cancer cases were recorded from the hospital based cancer registries of Coimbatore district. The cancer cases were segregated district-wise for specific cancer sites and the age-standardized incident rates were calculated for different age groups. Results:Coimbatore district recorded the highest number of incidences among all districts. Among all age-groups the adults aged 50-74 carry the highest burden of cancer. Among men, head and neck and gastrointestinal cancers are predominant while among women, breast and gynecological cancers are high. The age-standardized incidence rates were found to be higher in Coimbatore and least in Salem. Conclusion:Through this study, it is observed that Coimbatore district is under major threat and needs further investigation of risk factors for implementing optimized treatment and prevention strategies for reducing the adverse effectsof cancer.
GEOSPATIAL MODELLING OF AIR POLLUTION AND ITS IMPACT ON HEALTH OF URBAN RESIDENTS USING SPATIAL MODELS: A REVIEW
(Springer Link, 2021-03-25) Prashanthi Devi, M; Janani, Selvaraj; Harathi, Dayalan
Air quality is a very important factor in projecting or representing the status of environment and health of any region particularly urban areas. Air pollution studies analysing the quality of air deliver strategic information to the decision-making process and play a significant role in the implementation of the policies that influence the air quality of a region. Majority of the air pollution models that simulate the distribution of pollutants consider various physical and environmental characteristics that include wind direction, speed, temperature, etc., which help in determination of the air pollution trajectory. The integration of these models in GIS gives a geophysical dimension to the air quality information by relating the actual pollution concentrations to the health of plant and human life in that location. Over the recent years, several efforts have been made to map traffic-related emission and determine pollution patterns in urban areas using GIS. The use of GIS as a tool to illustrate the spatial patterns of emission and to visualize the impact of congestion on human health has long been attempted. To simulate the impact of air quality in terms of transportation and land use policy changes, several integrated models can be performed. GIS is a dynamic tool when combined with statistical analysis to map traffic-related air pollution and to generate predictive models of pollution surfaces. These models are useful to develop decisions based on monitored pollution data and exogenous information. With this background, a review on GIS-based methods to evaluate the impact of air pollution on human health has been presented. The complexity of using GIS for integrated air quality mapping and its impact on human health lies in many domains. Understanding the relationships between health, environment, geology, hydrology, air pollution studies, agronomy and their dependencies in a spatial phenomenon is the major crux. Spatial explorative models that can determine the relationships between the environment and high pollution concentrations across various demographic layers can help in identifying hotspots that demand special investigation or monitoring. Data visualization which otherwise means illustrating complex information through a map provides a dynamic insight to help the authorities plan future strategies.
SPATIAL DATA MINING USING ASSOCIATION RULES AND FUZZY LOGIC FOR AUTONOMOUS EXPLORATION OF GEO-REFERENCED CANCER DATA IN WESTERN TAMILNADU, INDIA
(SpringerLink, 2015-08-12) Harathi, Parasur Babu; Janani, Selvaraj; Sridhar, Ramachandran; Prashanthi Devi, Marimuthu; Balasubramanian, Somanathan
Data mining using association rule is widely applied in medicine, particularly in cancer epidemiology. It is reported that this technique has certain uncertainty. To minimize the uncertainty, fuzzy logic is used with association rules. To demonstrate the efficiency of these methods further, geographical information system tool is used to spatially view results obtained from above-mentioned techniques. For the present study, cancer data were taken due its disparity among different populations/locations and also because it is a serious concern that affects our socio-economic well being. Cancer is a family of diseases arising due to varied factors and there is no one cause and cure until the definite causative factor is determined. Data mining approach using association rule technique was applied to extract association between diet and incidences of cancer and was interpreted using fuzzy logic. The spatial data were displayed through map objects, and apriori algorithm is used to evaluate, visualize, and analyze the results from the data mining process. In this regard, data consisting of 3000 cancer cases were scrutinized which involves 16 parameters, 160 types of cancer, and 5 types of dietary habits including smoking, mixed diet, alcohol, betel nut, and tobacco chewing. Association rule mining reduces 800 combinations of cancer and habits to 129 cancer types and 3 habits and plots the respective location in the map through map objects. Fuzzy logic is used to find the spatio-habits linked. Association rule integrated with fuzzy logic reveals the influence of diet on cancer and its spatial pattern of the disease distribution. This technique enables us to provide the interpretation for the severity of disease that needs further attention and decision making.
SPATIAL DATA MINING USING ASSOCIATION RULES AND FUZZY LOGIC FOR AUTONOMOUS EXPLORATION OF GEO-REFERENCED CANCER DATA IN WESTERN TAMILNADU, INDIA
(Springer Link, 2015-08-12) Harathi, Parasur Babu; Janani, Selvaraj; Sridhar, Ramachandran; Prashanthi Devi, Marimuthu; Balasubramanian, Somanathan
Data mining using association rule is widely applied in medicine, particularly in cancer epidemiology. It is reported that this technique has certain uncertainty. To minimize the uncertainty, fuzzy logic is used with association rules. To demonstrate the efficiency of these methods further, geographical information system tool is used to spatially view results obtained from above-mentioned techniques. For the present study, cancer data were taken due its disparity among different populations/locations and also because it is a serious concern that affects our socio-economic well being. Cancer is a family of diseases arising due to varied factors and there is no one cause and cure until the definite causative factor is determined. Data mining approach using association rule technique was applied to extract association between diet and incidences of cancer and was interpreted using fuzzy logic. The spatial data were displayed through map objects, and apriori algorithm is used to evaluate, visualize, and analyze the results from the data mining process. In this regard, data consisting of 3000 cancer cases were scrutinized which involves 16 parameters, 160 types of cancer, and 5 types of dietary habits including smoking, mixed diet, alcohol, betel nut, and tobacco chewing. Association rule mining reduces 800 combinations of cancer and habits to 129 cancer types and 3 habits and plots the respective location in the map through map objects. Fuzzy logic is used to find the spatio-habits linked. Association rule integrated with fuzzy logic reveals the influence of diet on cancer and its spatial pattern of the disease distribution. This technique enables us to provide the interpretation for the severity of disease that needs further attention and decision making.