Ahmed Kawsar, Emran Abdullah Al, Jesmin Tasnuba, Mukti Roushney Fatima, Rahman Md Zamilur, Ahmed Farzana
Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.
Asian Pac J Cancer Prev. 2013;14(1):595-8. doi: 10.7314/apjcp.2013.14.1.595.
Lung cancer is the leading cause of cancer death worldwide Therefore, identification of genetic as well as environmental factors is very important in developing novel methods of lung cancer prevention. However, this is a multi-layered problem. Therefore a lung cancer risk prediction system is here proposed which is easy, cost effective and time saving.
Initially 400 cancer and non-cancer patients' data were collected from different diagnostic centres, pre-processed and clustered using a K-means clustering algorithm for identifying relevant and non-relevant data. Next significant frequent patterns are discovered using AprioriTid and a decision tree algorithm.
Finally using the significant pattern prediction tools for a lung cancer prediction system were developed. This lung cancer risk prediction system should prove helpful in detection of a person's predisposition for lung cancer.
Most of people of Bangladesh do not even know they have lung cancer and the majority of cases are diagnosed at late stages when cure is impossible. Therefore early prediction of lung cancer should play a pivotal role in the diagnosis process and for an effective preventive strategy.
肺癌是全球癌症死亡的主要原因。因此，识别遗传和环境因素对于开发新的肺癌预防方法非常重要。然而，这是一个多层次的问题。因此，本文提出了一种肺癌风险预测系统，该系统简单、经济高效且节省时间。
最初从不同诊断中心收集了400名癌症和非癌症患者的数据，进行预处理，并使用K均值聚类算法进行聚类，以识别相关和不相关数据。接下来，使用AprioriTid和决策树算法发现显著频繁模式。
最终开发了用于肺癌预测系统的显著模式预测工具。该肺癌风险预测系统应有助于检测一个人患肺癌的易感性。
孟加拉国大多数人甚至不知道自己患有肺癌，大多数病例在无法治愈的晚期才被诊断出来。因此，肺癌的早期预测应在诊断过程和有效的预防策略中发挥关键作用。
