## 2 Materials and Methods
### Exploratory Data Analysis  
Exploratory data analysis (EDA) stands as a cornerstone in predictive analytics, serving to illuminate feature interactions, unveil correlations, discern valuable patterns, and foster data comprehension before venturing into predictive modeling. In our endeavor to unlock insights within the BRFSS dataset pertaining to heart disease, we leveraged various data analysis methodologies, including descriptive statistics, data visualization, and correlation analysis. A preliminary statistical examination unveiled pivotal characteristics embedded within our dataset: \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Description** \\ \hline General\_Health & The general health condition of the respondent \\ Checkup & The period elapsed since the last time the respondent had a routine check-up \\ Exercise & Whether the respondent participated in any physical activities during the last month or not \\ Skin\_Cancer & Whether the respondent had skin cancer or not \\ Other\_Cancer & Whether the respondent had another kind of cancer or not \\ Depression & Whether the respondent had a depressive disorder or not \\ Diabetes & Whether the respondent had diabetes or not \\ Arthritis & Whether the respondent had arthritis or not \\ Sex & The respondent’s sex \\ Age\_Category & The category of age that the respondent falls into \\ Height\_(cm) & The respondent’s height measured in cm \\ Weight\_(kg) & The respondent’s weight measured in kg \\ BMI & The respondent’s body mass index \\ Smoking\_History & Whether the respondent had a smoking history or not \\ Alcohol\_Consumption & The respondent’s reported alcohol consumption \\ Fruit\_Consumption & The respondent’s reported fruit consumption \\ Green\_Vegetables\_Consumption & The respondent’s reported green vegetables consumption \\ Fried\_Potato\_Consumption & The respondent’s reported fried potatoes consumption \\ Heart\_Disease & Whether the respondent reported having a heart disease or not \\ \hline \hline \end{tabular}
\end{table} 
Table 1: BRFSS dataset description. * The Sex distribution indicates a slight predominance of females over males, with females comprising 52% of the population. * Patient demographics span diverse age categories, with the highest representation observed in the 50-54 age group, closely trailed by the 55-59 and 60-64 age brackets. Notably, fewer young individuals participated in the survey, suggesting the potential applicability of the predictive model to older demographics. * The majority of patients perceive their overall health as \"Good\", with \"Very Good\" emerging as the next frequently chosen option. Relatively fewer patients (33%) categorize their health as \"Fair\" or \"Poor\". * A significant portion of patients (77%) underwent a medical checkup within the previous year. * Most patients reported no history of diabetes, arthritis, cancer, or depression. * The majority of patients had no smoking history (59%) and engaged in regular exercise (78%). Furthermore, gaining precise insights into the distribution of the target variable within the dataset is imperative. Our analysis of the BRFSS data unveiled a notable class imbalance, as depicted in Figure 2. Merely 8.1% of the surveyed population reported a diagnosis of heart disease, accentuating the challenge of class imbalance in our modeling endeavors. Such imbalance may predispose the model towards the majority class of non-CVD cases, potentially compromising its efficacy in identifying high-risk patients. Addressing this imbalance is paramount to mitigating bias and enhancing model performance in risk prediction. Afterwards, we conducted a correlation analysis, which shed light on the intricate relationships between various features and the risk of developing CVD. One notable observation is the negative correlation between general health and both heart disease and diabetes.