International Journals
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Item 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 SomanathanData 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.Item IDENTIFICATION AND MOLECULAR CHARACTERIZATION OF SHIGELLA FLEXNERI ST-02 FROM URINARY TRACT INFECTED PATIENT BY 16S RIBOSOMAL RNA GENE PARTIAL SEQUENCE ANALYSIS(Indian Journal Of Natural Sciences, 2014-10) Sridhar Ramachandran; N, Aarthi; V, Hemamalini; P, Pandia Vadivu; B, Mohan Kumar; S, ThiyagarajanUrinary Tract Infection (UTI) is mainly due to the entry of microorganisms and start developing to multiply in the urinary bladder. Cystitis and Urethritis are the two most common UTI among infected patients and mostly affect the bladder and urethra. The UTIs contribute significantly to the cost of providing health care in economically developed countries and it may be symptomatic or asymptomatic. Several studies such as multi-drug resistant strain, Extended Spectrum Beta Lactamase producing strain, recurrent urinary tract infection, symptomatic Shigella sonnei,UTI in pregnancy, polymicrobial septicemia etc. had been conducted with reference to Shigella flexneri. However, a study on Uropathogenic and their molecular typing and characterisation of Shigella flexneri using 16s r RNA gene sequencing is scanty. In the present study, the investigators were isolated Shigella species from the UTI patient and identified using standard microbiological procedures. The isolated strain was further confirmed by 16S rRNA gene sequencing. The sequenced strain has been submitted to GENBANK, USA and received gene accession number (JX444058). The Electropherogram report has been generated by Quality Control of Applied Biosystem, Hyderabad and reported that the Shigella flexneri ST-02 consist of 939 base pairs. Finally the sequenced strain was subjected to bioinformatic tools such as BLAST and phylogenetic tree for explorative and comparative studies. To the best of our knowledge, this constitutes the first report in which Shigella flexneri clinical strain had been isolated for UTI patient and was characterized by molecular typing using 16s ribosomal RNA gene partial sequencing analysis.