## 4 Comparison between Medical Data Classification Methods  
The prediction of new data from big medical data is not an easy challenge. There are many algorithms used for classification or prediction. In this paper, we implemented them on medical big data using seven different classification algorithms to identify the best algorithm with the highest accuracy and lowest processing time. The classification algorithms do not reach a good accuracy on their own; they require preprocessing steps to enhance the data quality. To enhance the accuracy, preprocessing steps and the feature selection method were used. The preprocessing steps clean the data from noise and replace the missing data. The feature selection method allows the reduction of the data structure, which affects the accuracy and running time of the prediction step. These steps are explained in Figure 3.  
Data processing has major advantages. However, it also has many disadvantages, as follows:  
* Machine learning needs to train on huge datasets, and these should be unbiased as well as of good quality. As a result, there can be periods where we should wait for new data to be produced. * Sufficient time is required for the algorithms to learn how to achieve their purpose with accuracy. Machine learning also requires massive resources to function, which leads to additional computer power requirements [37]. Figure 3: The proposed medical data classification model. From a data mining perspective, medical data classification is a process that requires the following main steps: (I) Data acquisition, (II) data preprocessing, (III) feature extraction, and (IV) data classification. Figure 3 shows the steps of preprocessing, feature selection, and classification. In the following sub-sections, we discuss these steps. 1. Data Acquisition  
Raw data are obtained from different sources since we use three different datasets. Then, to understand the data, we review the dataset attributes, values, and instances. 2. Data Preprocessing  
Raw data have many challenges due to the continuous collection of data from different medical sources. The analysis of this data results in a low accuracy value with inaccurate prediction results. The three datasets used have many redundant values and a set of missing data. Therefore, in this step, we clean the dataset by removing duplications and replacing the missing data with the \"Unknown\" value for categorical data and the mean value for the numerical data. The output of this step is a clean and high-quality dataset, which increases the accuracy of the used algorithms. 3. Feature Selection  
Data selection is conducted by finding standard features of data, which represent the original data. The featured dataset is the input of the classification step, rather than the full data. The feature selection step is one of the most important steps. It reduces data by focusing on the featured data. There are many feature selection algorithms, such as principal component analysis (PCA), singular value decomposition (SVD), CFS, etc. The most effective feature selection algorithm with our data is the CFS. It is suitable for our data parameters, which are numerical and textual data. CFS can improve the classification accuracy and efficiency by removing redundant features. Let X be the set of all the features of the dataset with a large number of features {f\({}_{1}\), f\({}_{2}\), f\({}_{3}\),..., f\({}_{n}\)}, where n is the number of the attributes or features of the dataset. The feature selection process involves selecting the data, which generates \(\times\) set of features with a small number of features [38]. 4. Data Classification  
Data classification has two stages, which are the training stage and the testing stage. In the training stage, part of the preprocessed data, which is 70%, is inputted into a defined classification algorithm that generates a training model. Then, in the testing stage, the other part of the preprocessed data, which is 30%, is inputted into the tested model to evaluate the defined classification algorithm.