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# 数据科学代写|数据分析代写Data Analysis代考|BISM3206 Data understanding

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## 数据科学代写|数据分析代写Data Analysis代考|Data understanding

This research was carried out using physical examination, laboratory findings and radiologic findings, depicted in Table 1.1. Data were obtained from IUC after the Ethical Committee’s approval.

This dataset contains 10 attributes of 130 patients. Each measurement vector consists of 10 values – seven attributes are binary and three attributes are continuous. The binary and continuous attribute values are mapped to zero and one, where zero refers to false (normal) and one refers to true (abnormal).

This dataset contains five diseases. These are Plummer disease, toxic multi-nodular goiter, Hashimoto’s disease, Graves’ disease and subacute thyroiditis. In this context, the number of target attributes are seven for Plummer disease, 40 for toxic multi-nodular goiter, 32 for Hashimoto’s disease, 48 for Graves’ disease and three for subacute thyroiditis for multiple classifications, as shown in Figure 1.1.

## 数据科学代写|数据分析代写Data Analysis代考|Modeling

For five different diseases, analyses were performed using machine learning methods. SVM, k-nearest neighbors ( $\mathrm{KNN})$, artificial neural network (ANN) and decision tree (DT) were used. With these algorithms, fivefold cross-validation was used as a performance evaluation method for the dataset before the models were performed. According to this method, the dataset is divided into five equal parts each time, one part is chosen to be tested and the others are used as training data.
The accuracy metric in equation [1.1], the precision metric in equation [1.2], the recall metric in equation [1.3] and F-measure metric in equation [1.4] are widely used for model performance. In this study, accuracy was selected as the model performance evaluation metric.
\begin{aligned} &\text { Accuracy }=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+\mathrm{FP}+\mathrm{FN}} \ &\text { Precision }=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \ &\text { Recall }=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \ &F-\text { Measure }=2 * \frac{\text { Precision } * \text { Recall }}{\text { Precision }+\text { Recall }} \end{aligned}

## 数据科学代写数据分析代写Data Analysis代考|Modeling

Accuracy $=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+\mathrm{FP}+\mathrm{FN}} \quad$ Precision $=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}$ Recall $=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \quad F-$ Measure $=2 * \frac{\text { Precision } *}{\text { Precision }+\text { Recall }}$

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。