Using convolutional neural networks with late fusion to predict heart disease.

Cardiovascular diseases are responsible for one-third of all deaths that occur globally. Machine learning and data mining have made it easier and quicker for physicians to diagnose or identify patients. This article presents a novel late fusion method over convolutional neural networks for predicting heart disease. The data was sourced from the UCI Machine Learning Repository for this research. The sample is composed of 303 instances and 13 features. This is known as the 'late fusion' deep learning technique, combining data from multiple sources to produce more accurate predictions. Thus, specialized models perform independent data analysis in this manner, and the results are aggregated to provide an extended forecast. CNNs are designed with specificity in mind to handle different kinds of data modalities. Conversely, DNNs can obtain comprehensive information and analyze tabular data. Here, we also found that our model was highly accurate in identifying heart illnesses with the help of CNNs and DNNs in an effective manner. A new hybrid architecture was designed to merge numerical features with graphics, capturing the dataset's spatial and sequential properties. The approach we adopted in our research yielded fruitful outcomes after a careful assessment of the model. The validation and testing set had zero percent error for accuracy, precision, recall, and an F1 score of 99.99%. This work's valuable contribution to the field of medical diagnostics provides a strong foundation for further exploration and simplifies the task of creating precise and extensible algorithms for predicting heart disease. Through this, patient health is enhanced, and medical data management is improved.