Forecasting and Recognition of Coronary Illness Utilizing Clinical Data Mining Techniques
Abstract
Coronary illness, a prevalent global health concern, profoundly impacts human life. With cardiovascular diseases leading to a significant number of deaths worldwide, early and precise diagnosis is crucial for effective prevention and treatment. This study focuses on utilizing the Heart Stalog dataset from the UCI repository and employs Random Forest and Logistic Regression algorithms to predict coronary illness occurrences. Our findings indicate that the Logistic Regression model achieved the highest overall accuracy rate of 83%, outperforming the Random Forest model. This research underscores the importance of developing accurate and efficient classifiers in Data Mining for clinical applications.
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Introduction
In recent years, there has been a significant surge in the interest in analyzing clinical data, driven by the recognition of its potential to integrate patient information from diverse sources into cohesive datasets for better healthcare management. This necessitates the utilization of various technologies, including Data Mining, Machine Learning, Artificial Intelligence, and Data Visualization.
Healthcare institutions are generating vast amounts of data, posing challenges in data management. Hospitals, in particular, accumulate extensive patient information and medical records. Data mining seeks to uncover patterns and relationships within this data, providing valuable insights for decision-making. Clinical data mining plays a crucial role in extracting meaningful information from healthcare datasets.
Various medical conditions, such as heart disease, liver failure, kidney dysfunction, nerve damage, and vision loss, underscore the importance of early detection. Detecting diseases like diabetes in their early stages is critical. The heart, being the central organ, affects the functioning of other bodily systems. Regular heart disease screenings are essential.
Heart disease, characterized by the heart's inability to pump sufficient blood to meet the body's needs, is among the most complex and deadliest human ailments. According to the World Health Organization, millions die annually from cardiovascular diseases like heart attacks and strokes.
Symptoms of heart disease include shortness of breath, physical weakness, swollen feet, and fatigue, accompanied by indicators like elevated jugular venous pressure and peripheral edema. Early diagnosis methods for heart disease were historically complex, contributing to the disease's severity.
Diagnosing and treating heart disease, especially in non-industrialized nations, is challenging due to limited diagnostic tools, medical personnel, and resources. Accurate diagnosis of heart disease risk is crucial for mitigating associated risks and improving patient outcomes. [1,6,7]
Conclusion
The clinical dataset in the different information mining and the AI strategies are accessible and afterward the significant part of clinical information mining is to build the exactness and productivity of infection determination. The target of this exploration work is meant to show the classes of Heart Stalog illness from the accessible crude clinical dataset assists the doctor with showing up at a precise determination to expect if a Heart infection will be missing or introduce. Considering the examination of the results, Logistic Regression has a most raised gauge precision of 83%. This is the best model to anticipate patients with coronary illness. Subsequently, proposed Logistic Regression Classifier approach will yield a successful technique for both forecast and recognition.