## 4 Data Collection and Preprocessing
### Data Collection  
Data collection in healthcare systems refers to the process of gathering and recording information about patients, medical conditions, treatments, and various healthcare-related factors. Typically, providing up-to-date information regarding the patient's condition can be very helpful to medical professionals. The availability of such information allows a reliable cardiovascular disease prediction to be achieved. The primary purpose of data collection in healthcare systems is to collect and maintain patient information to monitor health status, provide treatment, and make informed clinical decisions. In particular, this data is essential for managing patient care, enabling timely decisions using the patterns that exist in the data, healthcare administration, developing strategies for health promotion and disease prevention, and decision-making within the healthcare system [37]. Data collection in healthcare can involve various sources, including electronic health records (EHRs), ECG, IoT devices that can be kept in a body, time-series data, clinical assessments, medical records, wearable devices, patient-reported data, and medical imaging. EHRs in particular contain a wealth of patient information, including demographics, medical history, vital signs, and diagnostic tests [152]. EHRs are a primary source of healthcare data for cardiovascular disease prediction. Wearable devices like smartwatches and fitness trackers can collect real-time data on heart rate, activity levels, and sleep patterns. These devices are used to collect data and monitor and predict cardiovascular disease risk. Medical imaging, including echocardiograms, MRIs, and CT scans, provides detailed information about cardiac structure and function. Note that integrating data from such various sources allows for more comprehensive patient profiling and accurate predictions [153]. The data produced by the sensors exhibit the traits of significant volume, speed, and diversity, typical of big data [154]. Collecting patient-reported data, such as symptoms and lifestyle factors, can engage patients in their care and help healthcare professionals better understand individual health needs. The collected data can be structured (e.g., numerical measurements and categorical information) or unstructured (e.g., clinical notes and medical images) [32]. This data can be analyzed to identify trends, assess outcomes, and develop new medical insights. Data collection is often necessary to comply with healthcare regulations and quality reporting requirements. Hence, accurate and secure data handling is critical for regulatory compliance. Healthcare data can also have missing values, errors, and inconsistencies. Therefore, ensuring data quality is critical for accurate modeling. Current works in healthcare systems [155; 156; 157] predict data uniformly, so neglecting urgency. This indeed would cause delays in treating severe patient conditions. Besides, managing, storing, and processing this data in real time can be a significant challenge. To this end, in [158] medical decision assistance is defined as furnishing clinicians with intelligently filtered computer-generated clinical knowledge and patient-related information to improve patient care. Various clinical databases are commonly used for cardiovascular disease prediction. These databases include Cleveland cardiovascular disease dataset obtained from the University of California Irvine (UCI) [159], the Framingham cardiovascular disease prediction dataset [160], Cardiovascular Disease dataset [161], Physikalisch Technische Bundesanstalt (PTB) diagnostic ECG dataset [162], and the stroke prediction dataset [163]. Researchers often use these databases for cardiovascular disease research. Overall, data collection in healthcare systems can vary widely. There are some technical challenges related to healthcare data collection such as data privacy, reliability, and security. In particular, healthcare data is highly sensitive, and patient privacy is a paramount concern.