用8种机器学习模型预测慢性肾病，随机森林准确率达99%。
Performance Analysis of Machine Learning Algorithms in Chronic Kidney Disease Prediction
- 用均值-众数和随机采样填补缺失数据，再训练8类模型
- 随机森林与逻辑回归准确率最高，达99%，KNN最低仅73%
- 适合医疗诊断系统开发者参考模型选型
肾脏是人体的过滤器官。全球约10%的人口受慢性肾病（CKD）影响，导致肾功能逐渐下降。为保护高危患者免受进一步损害，有效的风险评估与及时监测至关重要。由于机器学习具备快速精准的检测能力，现大量医疗诊断系统依赖其疾病预测性能。本研究基于UCL机器学习库的CKD数据集（含少量缺失值），采用均值-众数法与随机采样法填补缺失数据，随后运用8种机器学习算法（随机森林、SVM、朴素贝叶斯、逻辑回归、KNN、XGBoost、决策树、AdaBoost）构建预测模型，并通过准确率对比评估各模型表现。结果显示，随机森林与逻辑回归模型准确率均为99%，表现最优；其次为AdaBoost、XGBoost、朴素贝叶斯、决策树和SVM；KNN模型准确率最低，为73%。
原文摘要 · Abstract (English)
Kidneys are the filter of the human body. About 10% of the global population is thought to be affected by Chronic Kidney Disease (CKD), which causes kidney function to decline. To protect in danger patients from additional kidney damage, effective risk evaluation of CKD and appropriate CKD monitoring are crucial. Due to quick and precise detection capabilities, Machine Learning models can help practitioners accomplish this goal efficiently; therefore, an enormous number of diagnosis systems and processes in the healthcare sector nowadays are relying on machine learning due to its disease prediction capability. In this study, we designed and suggested disease predictive computer-aided designs for the diagnosis of CKD. The dataset for CKD is attained from the repository of machine learning of UCL, with a few missing values; those are filled in using "mean-mode" and "Random sampling method" strategies. After successfully achieving the missing data, eight ML techniques (Random Forest, SVM, Naive Bayes, Logistic Regression, KNN, XGBoost, Decision Tree, and AdaBoost) were used to establish models, and the performance evaluation comparisons among the result accuracies are measured by the techniques to find the machine learning models with the highest accuracy. Among them, Random Forest as well as Logistic Regression showed an outstanding 99% accuracy, followed by the Ada Boost, XGBoost, Naive Bayes, Decision Tree, and SVM, whereas the KNN classifier model stands last with an accuracy of 73%.
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