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Why use MDS and PCA on fetal health data? I get fetal dataset contains 2126 records of features extracted from Cardiotocogram exams, which were then classified by three expert obstetritians into 3 classes: Normal, Suspect, and Pathological. And all I need to do is to classify the health status of the fetus (fetal_health) through these feature values and built a predictive model. But firstly, I want to reduce dimensionality of the data, because it contains 22 features, so using MDS to visualize the relative positions of variables, and then using PCA to reduce dimensions in order to get a simple version of dataset.
Why use Logistic Regression? Logistic Regression is a generalized linear regression analysis model, which is often used in data mining, automatic disease diagnosis, economic forecasting and other fields. For example, explore the risk factors that cause diseases, and predict the probability of disease occurrence based on risk factors.
My main idea for offering this topic is interest in dimension reduction and prediction models, and also classifying fetal health data can prevent fetal and maternal mortality which is meaningful in itself.
Fetal health data comes from Kaggle. This dataset contains 2126 rows and 22 features which extracted from Cardiotocogram exams, which were then classified by three expert obstetritians into 3 classes: normal, suspect and pathological. Dataset authors: Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318
---如何运用MDS/PCA数据减维方法处理健康领域数据并且不降低预测准确性
这是我在波兰大学第一学期的无监督机器学习的论文。
全文详细地址为https://rpubs.com/Ting_Wei/990760
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