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, 2014-09-30) 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 and Methods: 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 effects of cancer
SPACE TIME MODEL FOR CANCER INCIDENCES IN TAMIL NADU: MAPPING HEALTH STATISTICS FOR POLICY PROGRAMMING AND DECISION MAKING
(International Journal of Advanced Research in Computer Science and Software Engineering, 2015-04) P B, Harathi; Janani Selvaraj; M, Prashanthi Devi
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. In specific the geographical study of cancer will help in identifying the high risk communities for further etiological studies. The objective of the present study is to analyze the time based geographical expansion of cancer incidences in the study region. The spatialtemporal model using Knox and Mantel statistic was applied to identify if additional cases are added in subsequent time period from high incidence areas or from moderate areas or from low incidence areas. This study will provide an indication to any association between time trend and cancer incidences. Through the spatial temporal model, the high risk areas have been identified and the temporal variations in the risky zones were assessed
SPATIAL ANALYSIS OF CANCER INCIDENCES TO IDENTIFY RISK AREAS AND HOT SPOTS: A CASE STUDY IN THE WESTERN REGIONS OF TAMIL NADU, INDIA
(International Journal of Scientific Research, 2014-07) P B, Harathi; Janani Selvaraj; M, Prashanthi Devi; S, Valarmathi; S, Balasubramanian
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. In specific the geographical study of cancer will help in identifying the high risk communities for further etiological studies. Objective: The present study aims to investigate the application of various spatial statistical tools to identify the high cancer risk zones in the western regions of Tamil Nadu, India. Methodology: Spatial point pattern analysis was performed to assess the area based risk factor for cancer in the study area. The cancer incidences recorded in each address were geo-coded to build point features. Dual kernel estimation method was used to simplify the complex point patterns without diminishing the significance of the incidence level data. The incident hot spots were verified and tested for their statistical significance against a random distribution by means of Nearest Neighborhood Index, Ripley’s K, Geary’s C and Moran’s I test. CrimeStat software (CrimeStat III, 2004) and ArcGIS 9.1 were used to obtain these results. Results and Conclusion: The smoothed map produced through the Kernel estimation method showed high clustering in the Coimbatore North, Coimbatore South and Erode taluks and was confirmed statistically by the Nearest Neighbouhood Index and Ripley’s K test. Further, from the values obtained by the Moran’s I and Geary’s C test it is observed that there exists positive partial autocorrelation in the point data. Hence the spatial analytical methods will be useful tools in conducting further etiological studies in the high risk regions. In addition, it will be also helpful for the health professionals to organize early cancer screening programs and better prevention strategies for the society
SPATIAL DATA MINING USING ASSOCIATION RULES AND FUZZY LOGIC FOR AUTONOMOUS EXPLORATION OF GEO-REFERENCED CANCER DATA IN WESTERN TAMIL NADU, INDIA
(Netw Model Anal Health Inform Bioinforma, 2005-12-01) 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.