Transparent Healthcare: Unraveling Heart Disease Diagnosis with Machine Learning


  • Junaid Abbas Department of Computer Science, University of Malashiya, Asia


Transparent Healthcare, Machine Learning, Heart Disease Diagnosis, Predictive Model, Interpretability, Healthcare Professionals, Patient Trust, Supervised Learning, Deep Neural Networks, Early Intervention


In the realm of modern healthcare, the integration of machine learning (ML) technologies has become pivotal, revolutionizing traditional diagnostic approaches. This study delves into the application of ML algorithms for unraveling heart disease diagnosis, emphasizing the importance of transparency in enhancing both the accuracy of predictions and the trust of healthcare professionals and patients alike. Our research leverages extensive datasets, encompassing diverse patient profiles and clinical parameters. Through the implementation of advanced ML techniques, including supervised learning and deep neural networks, we develop a sophisticated model for heart disease identification. This model not only excels in predictive accuracy but also prioritizes interpretability, allowing healthcare professionals to comprehend the intricate relationships between various contributing factors. The transparency of our ML model is achieved by elucidating the key features influencing diagnostic outcomes. This transparency is crucial in demystifying the decision-making process of the algorithm, fostering trust among healthcare practitioners and empowering them to make informed decisions based on the model's insights. Validation experiments conducted on real-world datasets demonstrate the superior performance of our approach compared to conventional diagnostic methods