# Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators
## 3 Materials and Methods
### Dataset Description and Statistics  
The Cleveland heart dataset consists of 303 instances with 76 attributes, but only 14 attributes are considered more suitable for research experimental purposes. The attribute descriptions for the Cleveland heart dataset are given in Table 1.  
### Dataset Description and Statistics  
The Cleveland heart dataset consists of 303 instances with 76 attributes, but only 14 attributes are considered more suitable for research experimental purposes. The attribute descriptions for the Cleveland heart dataset are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attribute** & **Description** & \
\begin{tabular}{c} **Type of** \\ **Attribute** \\ \end{tabular} & **Attribute Value Range** \\ \hline age & Age in years & Numeric & 29 to 77 \\ \hline sex & Gender & Nominal & 0 = female, 1 = male \\ \hline cp & Chest pain type & Nominal & 1 = typical angina, 2 = atypical angina, 3 = non-angina pain, 4 = asymptomatic \\ \hline \multirow{2}{*}{trestbps} &
\begin{tabular}{c} Resting blood pressure in mm Hg \\ on admission to the hospital \\ \end{tabular} & Numeric & 94 to 200 \\ \hline chol & Serum cholesterol in mg/dL & Numeric & 126 to 564 \\ \hline \multirow{2}{*}{fbs} & \multirow{2}{*}{Fasting blood sugar \textgreater{} 120 mg/dL} & Nominal & 0 = false, 1 = true \\ \hline \hline \end{tabular}
\end{table}
Table 1: Attribute descriptions for the Cleveland heart dataset from the UCI machine learning repository [33].  
Figure 1: Experimental workflow of the proposed methodology.  
The attributes with less than 10 classes are considered nominal or categorical types. The attribute'sex' consists of two classes based on gender: 1 = male and 0 = female. The attribute 'cp' contains four classes of chest pain types: 1 = typical angina, 2 = atypical angina, 3 = non-angina pain, and 4 = asymptomatic. The attribute 'fbs' includes two classes regarding whether the fasting blood sugar >120 mg/dL: 1 = true and 0 = false. The attribute'restecg' comprises three classes of resting electrocardiogram results: 0 = normal, 1 = abnormality in the ST-T wave, 2 = definite hypertrophy in the left ventricular. The attribute 'exang' consists of two classes based on exercise-induced angina: 1 = yes and 0 = no. The attribute'slope' includes three classes of peak exercise ST segment slope: 1 = upslope, 2 = flat, and 3 = downslope. The attribute 'ca' comprises four classes based on the number of major vessels (0-3) that are colored using fluoroscopy. The attribute 'thal' contains three classes of heart status: 3 = normal, 6 = fixed, and 7 = reversible. The attribute 'target' consists of five classes of prediction: 0 = no risk of heart disease, and 1 to 4 = the risk of heart disease in various stages. Since the main purpose of this research work was to predict whether a patient was at risk of developing heart disease, the values in the range 1 to 4 were converted to 1. Therefore, the 'target' attribute consisted of only two classes: 0 and 1. The attributes 'age,''restbps,' 'chol,' 'thalach,' and 'oldpeak' are considered as numeric/integer type attributes.  
The statistical characteristics of the numeric attributes, such as the minimum, maximum, mean, standard deviation, missing, distinct, and unique values, are provided in Table 2(a) There are no missing values found in the numeric attributes of the Cleveland dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
The statistical characteristics of the nominal attributes, such as label, count, missing, and distinct values, are provided in Table 2(b). There are six (6) instances in total out of 303 that were found to have missing values, which accounted for 2% of the whole dataset: four (4) from the 'ca' attribute, and two (2) from the 'thal' attribute. The target class labels 0 (no risk of heart disease) consisted of 164 instances and label 1 (risk of heart disease) consisted of 139 instances, which accounted for 54% and 46% of the dataset, respectively.