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168 CHAPTER 8 SUMMARY Interest in the patterns and predictability of various types of cancer has been steadily growing over the years (e.g. Steliarova-Foucher et al., 2004). However, comparative studies on geo-ethnic grounds (particularly Africa-specific) have been quite limited. Further, the influence of commonly known predisposing factors on various types of cancer has mainly been studied using conventional statistical analysis approaches which, typically, make assumptions about the underlying data distributions. We critically examine the common methods used in studying cancer predisposing factors and propose a novel triangular approachtodetectingpatternsindatasamples.Threesamplesofcervixcancer, conjunctival cancer and Kaposi’s sarcoma, collected from various regions in the United Republic of Tanzania, are subjected to techniques for graphical visualisation (GDV), association rules (AR) and dimensional reduction (DR). We uncover interesting patterns in the distribution of the three cancer types hinging on Tanzania’s basic cultural attributes and geographical diversity. The results are analysed in a multi-disciplinary context; presenting an unprecedented approach to addressing cancer-related research issues. Our findings show that the triangular approach is capable of uncovering subtle cultural-driven relationships among data attributes. The findings present a rigorous basis for assessing and evaluating the impact of predisposing factors on the three types of cancer hence providing some useful guidelines into informed intervention, prevention and treatment of the diseases. On the basis of the results from the analysis, the paper makes recommendations as to how to structure future cancer studies in areas of similar geo-ethnic features. The triangular approach to extracting knowledge from data provides good insights into avoiding knowledge-masking effects and highlights the need for formulating and designing a prototype model for updatable cancer-related data sources and storages aimed at connecting cancer registries across the African continent and beyond. A Consensual Approach to Domain-Partitioning of a Cancer Data Sample Space LessonsfromTanzania Kassim S. Mwitondi and Khamza K. Maunda 169 A CONSENSUAL APPROACH TO DOMAIN-PARTITIONING OF A CANCER DATA SAMPLE SPACE INTRODUCTION Interest in studying comparative patterns of various types of cancer has been steadily growing over the years (Steliarova-Foucher et al., 2004, Ames et al., 1995). Whereas cancer has been widely studied and results documented in the developed world, comparative studies on geo-ethnic grounds, particularly Africa-specific, have been rare and limited in scope. Recent initiatives include Mbulaiteye et al., (2006) who carried out a cancer risk study among HIV/AIDS victims in Uganda. The influence of commonly known predisposing factors on various types of cancer has mainly been studied using conventional statistical analysis approaches which, typically, make assumptions on the underlying data distributions. In this study we critically examine the common methods used in studying cancer predisposing factors and propose a novel triangular approach to detecting patterns in data samples. Three samples of cervix cancer, conjunctival cancer and Kaposi’s sarcoma, collected from various regions in the United Republic of Tanzania, are subjected to techniques for graphical visualisation (GDV), association rules (AR) and dimensional reduction (DR). We uncover interesting patterns in the distribution of the three cancer types hinging on Tanzania’s basic cultural attributes and geographical diversity. The paper seeks to answer the general question as to whether the provided data attributes lead to any informative cancer patterns on a geo-ethnic basis – that is: Is there empirical evidence of geo-ethnic inter-dependency between the three types of cancer and the collected data attributes? Validity of the questions lies in its potential for yielding useful diagnostic information. The study is based on samples of cervix cancer, conjunctival cancer and Kaposi’s sarcoma – all well-known HIV defining cancers (Mbulaiteye et al., 2006) – and their analyses seek to contribute to knowledge sources on the homogeneity/ heterogeneity of the various types of cancer on geographical and ethnocultural bases. Findings from the study are meant to strengthen and enhance comparability of the geo-demographic patterns of the diseases’ occurrence in order to determine the efficacy of their prediction across geographical and ethno-cultural lines. It is expected that drawing conclusions from multiple analytical approaches – graphical visualisation (GDV), association rules (AR) and dimensional reduction (DR) – and comparing the results in time and space may help in detecting potential shifts in concepts, definitions and the baseline behaviour of various types of cancer. This type of multi-disciplinary approach to cancer studies has never been used before. Using databases gathered from five continents, Kamangar et al., (2006) were able to explain variations in the global cancer patterns through a complex relationship...

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