## 4 Comparison between Medical Data Classification Methods
### Results  
Tables 4-6 show the comparison between different classification algorithms, such as NB, LM, R, DT, RF, GBT, and J48. The classification algorithms are tested with preprocessing data cleaning steps, such as duplication and missing data removal. The processing time for executing the tested algorithms on diabetes data is increased, compared to the other tested datasets. Whereas the data volume increased, the running time of algorithms increased, as shown in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Relative Error** & **Precision** & **Sensitivity** & **Time (s)** \\ \hline NB & 66.2 & 33.8 & 75.2 & 89.56 & 1.39 \\ LM & 69.96 & 30.04 & 70.43 & 72.67 & 3.9 \\ R & 74.4 & 25.6 & 69.73 & 70.02 & 8.04 \\ DT & 75 & 25 & 78.43 & 78.9 & 4.7 \\ RF & 75.2 & 24.8 & 89.52 & 99.06 & 9.12 \\ GBT & 74.1 & 25.9 & 87.4 & 90.02 & 16.79 \\ J48 & 72.9 & 27.1 & 70.42 & 84.1 & 86.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between different algorithms applied on heart disease data without preprocessing. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Attributes\({}^{\prime}\)** & **Instances\({}^{\prime}\)** & **Missing (Y/N)** & **Redundancy** & **Noise** \\  & **Number** & **Number** & **Data (Y/N)** & **(Y/N)** & **(Y/N)** \\ \hline M-Health [39] & 24 & 161281 & Y & Y & Y \\ Diabetes [40] & 50 & 101797 & Y & Y & N \\ Heart Disease [41] & 19 & 59077 & Y & N & Y \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the three experimented datasets. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Encoding (Y/N)** & **Feature Selection (Y/N)** & **Attributes’ Number** \\ \hline M-Health & N & Y & 12 \\ Diabetes & Y & Y & 29 \\ Heart Disease & Y & Y & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of the three datasets after preprocessing and feature selection. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Relative Error** & **Precision** & **Sensitivity** & **Time (s)** \\ \hline NB & 77.5 & 22.5 & 86.7 & 65 & **0.078** \\ LM & 82.5 & 17.5 & 78.3 & 90 & 3 \\ R & **90** & 10 & 86.4 & 95 & 2.95 \\ DT & 80 & 20 & 73.1 & 95 & 2.99 \\ RF & **90** & 10 & 83.3 & 100 & 3 \\ GBT & **90** & 10 & 83.3 & 100 & 16 \\ J48 & 84.56 & 15.44 & 84.6 & 84.6 & 85.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between different algorithms applied on preprocessed heart disease data. The experiments were evaluated using three datasets: Heart disease, M-health, and diabetes datasets. The classification algorithms were experimented by the Raddopo platform and MATLAB 2020a. In addition, the preprocessing and CFS reduction algorithms were implemented by the Waikato platform (Weka 3.9). Moreover, the classification algorithms were tested before and after applying the preprocessing step. From our studies, we conclude that the application of data reduction decreases the consuming time and preprocessing enhances the quality of the data, which lead to a better accuracy value. Tables 4 and 5 show the comparison between different classification algorithms on the heart disease dataset before and after applying preprocessing on the data. The tables present the accuracy, relative error, precision, sensitivity, and time of each algorithm. The mathematical analysis of error calculation is a critical portion of the measurement. This analysis detects the actual value and the error quantity. The relative error conducts how good or bad the classification is. In mathematical measurements, the errors are conducted by a round-off error or truncation error. In Tables 4 and 5, the results show that the accuracy after preprocessing is better than without preprocessing for all of the tested algorithms. Moreover, regarding time processing, the time processing of the algorithms without preprocessing is longer than the time processing of the preprocessed data.