Artificial Intelligence (AI) has rapidly become one of the most influential technologies shaping modern healthcare. Its integration into medical systems has improved several aspects of healthcare delivery, including disease diagnosis, patient monitoring, predictive analysis, and treatment planning. Among the many health problems affecting people globally, cardiovascular diseases (CVDs), particularly heart disease, continue to be the leading cause of death worldwide [7]. The rising number of cases highlights the importance of developing advanced diagnostic tools that can assist healthcare professionals in identifying risks at an early stage. Traditional approaches to heart disease diagnosis rely mainly on medical expertise, laboratory testing, diagnostic imaging, and manual interpretation of patient data. Although these methods have been widely used for many years, they often require specialized medical infrastructure, considerable time, and careful interpretation by experts. In some situations, human error or delayed diagnosis may affect the overall effectiveness of treatment. At the same time, the amount of medical data produced through electronic health records (EHRs), wearable health devices, and diagnostic imaging technologies has grown tremendously, making it difficult to analyze this information using conventional techniques alone [2]. Artificial Intelligence, particularly through Machine Learning (ML) and Deep Learning (DL), offers powerful methods for analyzing complex healthcare data. These technologies can identify hidden patterns within large datasets and help estimate the probability of heart disease by examining risk factors such as age, blood pressure, cholesterol levels, electrocardiographic results, lifestyle habits, diabetes history, and genetic background. AI-based systems often function as Clinical Decision Support Systems (CDSS), assisting healthcare professionals by providing insights that support faster and more accurate diagnostic decisions [10]. This research paper presents an analytical study of Artificial Intelligence applications in healthcare diagnosis, with special focus on heart disease prediction. The paper reviews existing research, explains the technologies used in AI-based diagnostic systems, and discusses methodological approaches, potential advantages, and challenges involved in implementing AI in healthcare environments. The findings indicate that AI-supported diagnostic systems have the potential to improve early disease detection, reduce mortality rates, increase healthcare accessibility, and enhance the efficiency of clinical decision-making [1].
Artificial Intelligence, Healthcare Diagnosis, Heart Disease Prediction, Machine Learning, Deep Learning, Clinical Decision Support Systems, Medical Data Analytics, Cardiovascular Diseases, Predictive Modeling, Healthcare Technology
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