Suuuuiiiii

After the COVID-19 pandemic, numerous surveys indicate that there has been an increase in cardiac arrests, particularly among middle-aged and elderly individuals. The concept involves developing an AI software capable of providing a probability assessment of potential causes of cardiac arrest in the future. The AI software will primarily analyze inputted datasets to offer an early prediction of potential causes of cardiac arrest based on the probability profile generated by the AI. The data analysis will focus on examining health reports of 100 individuals who have experienced cardiac arrest in various age groups. Input data will include age, ECG (electrocardiogram) reports (a crucial data input), blood reports, cholesterol levels, urine reports, and vital organ parameters such as liver and kidney profiles. This software aims to identify the probable causes of cardiac arrest and assign a probability to each potential cause. For example, out of 100 people, 20 may have had cardiac arrest due to high cholesterol, while 30 may have experienced it due to high blood pressure. For instance, by examining a patient's cholesterol level profile and utilizing an AI-generated model, early predictions of a potential cardiac arrest could be provided, expressed as a percentage. With more accurate data and a larger sample size, the software might even forecast a time interval, indicating when the patient is likely to experience a cardiac arrest. If this type of AI-generated machine learning software is integrated with general medical data, it has the potential to save a significant number of lives by enabling earlier intervention before unfortunate events occur. The AI will conduct modeling by studying health data. In the event of a potential cardiac arrest, individuals could go to the hospital and receive an immediate signal indicating the acute cause. The AI would analyze symptoms and reports, comparing them with previous medical reports. This approach enhances the ability to identify and address issues before they escalate.

Built With

• databases