On discovering co-location patterns in datasets: a case study of pollutants and child cancers

Abstract

We intend to identify relationships between cancer cases and pollutant emissions by proposing a novel co-location mining algorithm. In this context, we specifically attempt to understand whether there is a relationship between the location of a child diagnosed with cancer with any chemical combinations emitted from various facilities in that particular location. Colocation pattern mining intends to detect sets of spatial features frequently located in close proximity to each other. Most of the previous works in this domain are based on transaction-free apriori-like algorithms which are dependent on user-defined thresholds, and are designed for boolean data points. Due to the absence of a clear notion of transactions, it is nontrivial to use association rule mining techniques to tackle the co-location mining problem. Our proposed approach is focused on a grid based transactionization of the geographic space, and is designed to mine datasets with extended spatial objects. It is also capable of incorporating uncertainty of the existence of features to model real world scenarios more accurately. We eliminate the necessity of using a global threshold by introducing a statistical test to validate the significance of candidate co-location patterns and rules. Experiments on both synthetic and real datasets reveal that our algorithm can detect a considerable amount of statistically significant co-location patterns. In ⋆ The work is done when the author is at University of Alberta.

Predictive Factors of Advanced Colonic Adenomas and Cancer Using Data Mining

“…20,21 Having more than one patient with CRC in family, having under 60 years old patient in family, 21,22 and female sex of CRC patients in family are associated with the prevalence of advanced colonic neoplasms in participants. Smoking, also, was recognized as an important factor for advanced colonic neoplasms in male clusters and this result also admitted in study of Lee et al and Li et al 23,24 Furthermore, obesity was cited by Liu et al 25 as a risk factor and this study achieved reported this factor as well. The effect of obesity on the incidence of CRC was mentioned in study results of Bardou et al 10 Many people who anticipated to have cancer were under 50 years old and it seems that age of presenting cancer is declining, and this result admitted in paper of Smith et al 26 However, it has been shown in few clusters that old people are on the verge of cancer.…”

The index lift in data mining has a close relationship with the association measure relative risk in epidemiological studies

“…As for airborne pollutants, a total of 136 chemicals from the industry activities were registered in NPRI during the study period. The location of the emission sites, the average emission amount, and the predominant wind (direction and speed) at each site were used to create a dispersion region for the chemicals [23]. A pregnant woman was considered exposed to a chemical if her activity area (a 5 km radius from the center of the postal code of her residence) overlapped with the dispersion region of the chemical (Fig.…”

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