Results analysis using SVM.

Leukemia is a serious problem affecting both children and adults, leading to death if left untreated. Leukemia is a kind of blood cancer described by the rapid proliferation of abnormal blood cells. An early, trustworthy, and precise identification of leukemia is important to treating and saving patients’ lives. Acute and myelogenous lymphocytic, chronic and myelogenous leukemia are the four kinds of leukemia. Manual inspection of microscopic images is frequently used to identify these malignant growth cells. Leukemia symptoms include fatigue, a lack of enthusiasm, a dull appearance, recurring illnesses, and easy blood loss. Identifying subtypes of leukemia for specialized therapy is one of the hurdles in this area. The suggested work predicts and classifies leukemia subtypes in gene data CuMiDa (GSE9476) using feature selection and ML techniques. The Curated Microarray Database (CuMiDa) collected 64 samples representing five classes of leukemia genes out of 22283 genes. The proposed approach utilizes the 25 most differentiating selected features for classification using machine and deep learning techniques. This study has a classification accuracy of 96.15% using Random Fores, 92.30 using Linear Regression, 96.15% using SVM, and 100% using LSTM. Deep learning methods have been shown to outperform traditional methods in leukemia gene classification by utilizing specific features.白血病(Leukemia)是一类严重威胁儿童与成人健康的疾病，若未得到及时治疗可导致患者死亡。白血病是一类以异常血细胞快速增殖为特征的血液恶性肿瘤。早期、可靠且精准的白血病识别，对于患者的临床治疗与生命挽救至关重要。白血病主要分为四大类：急性淋巴细胞性、急性髓系、慢性淋巴细胞性与慢性髓系白血病。当前临床通常通过人工镜检显微图像的方式，识别这类恶性增殖细胞。白血病的典型症状包括乏力、精神萎靡、面色晦暗、反复感染以及易出血。针对白血病亚型开展精准治疗，是当前该研究领域的一大挑战。本研究提出的方案借助特征选择(feature selection)与机器学习(machine learning)技术，对基因数据集CuMiDa（GSE9476）中的白血病亚型进行预测与分类。精选微阵列数据库(Curated Microarray Database, CuMiDa)从22283个基因中，收集了涵盖5类白血病基因的64个样本。本研究采用筛选得到的25个最具区分度的特征，结合机器学习与深度学习(deep learning)技术完成分类任务。本研究的分类准确率如下：随机森林(Random Forest)为96.15%，线性回归(Linear Regression)为92.30%，支持向量机(SVM)为96.15%，长短期记忆网络(LSTM, Long Short-Term Memory)则达到了100%。研究表明，深度学习方法通过利用特异性特征，在白血病基因分类任务中的表现优于传统机器学习方法。