# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 4 Experimental Settings
### Data Set Generation
#### 4.1.2 Potential Deviation from the Data Set  
The original data set for this paper comes from many medical institutions, involving different areas. Specifically, there are 303 observations in Cleveland, and the data come from the Cleveland Clinic Foundation in Ohio. There are 294 observations in Hungarian, and the data are provided by the Hungarian Heart Institute. Hungary has also made some achievements in the research field of cardiovascular disease. These data reflect the features of heart disease in Hungary. The different geographical environment, living habits, and genetic background will make the prevalence rate and influencing factors of heart disease in this area different from other areas. There are 123 observations in Switzerland, with data from Zurich University Hospital and university of Basel Hospital. Long Beach, VA provided 200 observation values, and the data from the Medical Center of the Veterans Affairs Department of Long Beach were mainly aimed at the specific group of veterans, who were unique in life experience and health status, and their heart disease incidence might be different from that of the general population. These data are helpful for studying the heart disease problems of specific groups. Based on the above data, there may be some regional deviations in the data set, which are from Cleveland, Switzerland, and Long \begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Attribute Information** \\ \hline Age & age of the patient [years] \\ Sex & sex of the patient [M: male, F: female] \\ Chest PainType & chest pain type [TA: typical angina, ATA: atypical \\  & angina, NAP: non-anginal pain, ASY: asymptomatic] \\ RestingBP & resting blood pressure [mm Hg] \\ Cholesterol & serum cholesterol [mm/dL] \\ FastingBS & fasting blood sugar 1 [FOOTNOTE:1]Footnote 1: footnotemark: [1: if FastingBS \(>\) 120 mg/dL, 0: otherwise]. 2 [Normal: normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of \(>\)0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes’ criteria]. 3 [Numeric value between 60 and 202]. 4 [Up: upsloping, Flat: flat, Down: downsloping]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Attribute Information. Beach, USA. However, the sample size in different regions is quite different. Cleveland and Hungary have relatively large data sets, while Switzerland has a relatively small sample size. This difference may lead to the unbalanced capture of regional features by the model. Specifically, the model is likely to be more attuned to the features of regions with large sample sizes, while regions with small sample sizes will be inadequately represented, thus undermining the comprehensiveness and balance of the model's regional characteristic-capturing ability. There is another demographic deviation. The data of the Department of Veterans Affairs in Long Beach are aimed at veterans, who are different from the general population in terms of life experience and occupational exposure, and they may have unique risk factors for heart disease. However, the sample size of 200 veterans in the data set is not absolutely superior to that of other sources. When the model deals with the general population and veterans, this situation may lead to a lack of understanding of the characteristics of veterans. As a result, it may lead to the deviation of veterans' heart disease prediction. In addition, data collection may be biased, and medical institutions in different regions may have differences in data collection methods, standards, details, and so on. Different hospitals may use different testing equipment and methods to detect heart disease-related indexes. In addition, the specifications and details of the recorded data can be different, thus causing problems in data quality and consistency. When merging these data, noise may be introduced, which will affect the accurate learning and understanding of data features by the model. In addition, the types of diseases may also be biased.