Rajan Juliet Rani, Chelvan A Chilambu
Altern Ther Health Med. 2019 Jan 9;25(S1):2-7.
With the increase in population, there is a rise in number of cancer cases starting from young children to old people. The uncommon cancers are generally sporadic and there are no pre-defined techniques/tools for the diagnosis. Identifying the diseases at an early stage can avoid the cancerous cells from metastasis to different body parts through tissue, lymph system and blood. It is very difficult for the parents to know that the child is suffering from cancer until the cancer has reached to Stage 4. The duration it takes the cancer to reach Stage 4 can depend on many factors but the fact about childhood cancer is that it is curable to some extent. Diagnoses of the cancer at an early stage, i.e. at Stage 1, from childhood to old age can increase the survival rate of the patients by 85% and also helps to come up with certain therapy.
The Gene Expression data of Cancer is taken from the CGED. Two approached are being implemented in this paper: Modified version of the Support Vector Machine and Kohonen' s Self Organizing Map to identify the disease during its Stage 1. Annova method has been used to validate the data.
Support Vector Machine has yielded a classification accuracy of 99.1% and the Kohonen's map has produced an accuracy of 89% with the same set of samples.
Support Vector Machine has yielded a good accuracy result as opposed to Kohonen' s Self Organizing Map but SOM has the capability of adapting itself to learn new features based on experience unlike the SVM. A combination of both the tools can be used based on the type of patients visiting the practitioner. The approaches can assist the medical practitioners as pre-diagnoses tool for the early diagnoses of pediatric cancer.
随着人口增长，从幼儿到老年人的癌症病例数量不断上升。罕见癌症通常是散发性的，且没有预先定义的诊断技术/工具。在早期阶段识别疾病可以避免癌细胞通过组织、淋巴系统和血液转移到身体的不同部位。在癌症发展到4期之前，父母很难知道孩子患有癌症。癌症发展到4期所需的时间可能取决于许多因素，但儿童癌症的事实是它在一定程度上是可治愈的。从儿童到老年，在早期阶段（即1期）诊断癌症可以将患者的生存率提高85%，并且有助于提出某些治疗方法。
癌症的基因表达数据取自CGED。本文实施了两种方法：支持向量机的改进版本和科霍宁自组织映射，以在疾病的1期识别疾病。已使用方差分析方法来验证数据。
对于同一组样本，支持向量机的分类准确率为99.1%，科霍宁映射的准确率为89%。
与科霍宁自组织映射相比，支持向量机取得了良好的准确率结果，但与支持向量机不同，自组织映射有能力根据经验自我调整以学习新特征。可以根据就诊患者的类型结合使用这两种工具。这些方法可以帮助医生作为儿科癌症早期诊断的预诊断工具。
