## 2 Related Work
The authors experimented with five different classification algorithms, such as RF, NN, NB, R, and SVM. They concluded that the lowest-performing algorithm was the regression classification, while RF had a very high accuracy of 98.136%. The regression algorithm had the lowest accuracy value with a high volume of data. [PERSON] et al. [19] experimented with the Naive bayes (NB) algorithm. The authors concluded that the accuracy changed with the increase of data. When the data increased, the accuracy of the model decreased. In this experiment, the NB method achieved a 98.7% accuracy. Moreover, it is suitable for small datasets. Therefore, other algorithms should be experimented for a good accuracy value. [PERSON] et al. [20] proposed a method that classified the dataset into diabetes-affected patients and not affected ones using classification algorithms. The authors evaluated their model on real-world data, which were obtained from the Pima Indian population. They trained the model using six various algorithms, such as J48, MLP, HoeffdingTree, JRip, Bayes Net, and RF, and obtained a precision equal to 0.757 and a recall of 0.762. Although various algorithms were used, no single algorithm provided a sufficient accuracy value. In this context, experiments with new classification algorithms are required to provide high accuracy. The following researches implemented the DT algorithm for classification. [PERSON] et al. [21] tested the data using the python programming language. The output of the program displayed the risks of having heart disease. The authors used the DT and KNN algorithms for heart disease prediction. The DT algorithm tested the dataset to predict the chances of having heart disease and had an accuracy rate of 81%. In addition, the KNN algorithm tested the same dataset and had an accuracy rate or level of 67%. In the proposed work, we assume that the RF algorithm is more efficient, can be used for the automated work analysis, and enhances the accuracy value of work. Moreover, [PERSON] et al. [22] predicted diabetes-related heart disease, which is a kind of heart disease that affects diabetic people. Heart disease refers to a set of conditions that affect the heart or blood vessels. Although various data mining classification algorithms exist for heart disease prediction, there is inadequate data for heart disease prediction in a diabetic individual. Three different algorithms were implemented, including NB, SVM, and DT. Of note, the DT model consistently had higher accuracy than the NB and SVM models, with a 90% accuracy value. In the research by [PERSON] et al. [23], the J48 algorithm was used based on the chronic kidney disease (CKD) dataset. The CKD is categorized into various degrees of risk using standard markers. It is usually asymptomatic in its early stages, and early detection is important to reduce future risks. This study experimented with the cross industry standard process for data mining (CRISP-DM) methodology and the WEKA software to develop a system that can categorize the chronic condition of the kidney, depending on accuracy, sensitivity, specificity, and precision. The J48 algorithm provided the following results: 97.66% of accuracy, 96.13% of sensitivity, 98.78% of specificity, and 98.31% of precision. The following research by [PERSON] et al. [24] implemented the GBT algorithm. The authors predicted acute bronchiolitis for new children. The selection of suitable treatments is significant for disease progress. An extreme gradient boosting (XGB) classification algorithm, which is a machine learning method that is suggested in this paper, was used for medical treatment prediction. Four supervised machine learning algorithms incorporating KNN, DT, NB, and SVM were compared with the suggested XGB method. The results showed that the XGB had the highest prediction accuracy of 94%. In this context, the implementation of data reduction techniques is important to enhance prediction accuracy.