Abstract:
Heart disease continues to be a leading cause of mortality worldwide, emphasizing the importance of accurate early detection and prediction. This abstract presents a study aimed at developing a predictive model for heart disease using a Bayesian network approach. Bayesian networks are probabilistic graphical models that capture the dependencies between variables and provide a systematic framework for reasoning under uncertainty.
The study utilized a comprehensive dataset containing various clinical and demographic features of patients, including age, sex, blood pressure, cholesterol levels, and electrocardiogram (ECG) measurements. The dataset was obtained from a diverse population of individuals, encompassing both healthy and diseased subjects.
To build the Bayesian network model, a two-step process was followed. First, a domain expert was consulted to identify the relevant variables and their dependencies based on medical knowledge. Next, statistical techniques were employed to learn the conditional probability distributions of the variables from the dataset.
The developed Bayesian network model was assessed using a variety of performance metrics, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). The predictive model demonstrated promising results, achieving high accuracy in distinguishing between individuals with and without heart disease.
Furthermore, the Bayesian network model allowed for the identification of significant risk factors contributing to the development of heart disease. By analyzing the conditional probabilities and network structure, important insights regarding the relationships between different variables and their impact on heart disease risk were obtained.
The proposed approach holds significant potential for clinical applications, including early detection, risk assessment, and personalized treatment planning for individuals at risk of heart disease. The ability to accurately predict heart disease using a Bayesian network model can aid healthcare practitioners in making informed decisions and implementing preventive measures to reduce the burden of heart disease.
In conclusion, this study demonstrates the effectiveness of a Bayesian network model in predicting heart disease. The model provides a comprehensive and interpretable framework for understanding the complex relationships among various risk factors. Further research and validation are warranted to enhance the model’s robustness and generalizability, ultimately benefiting patients and healthcare providers in the fight against heart disease.
PREDICTION OF HEART DISEASE USING BAYESIAN NETWORK MODEL, GET MOREÂ Â COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
