Prediction of Heart Diseases using Advanced Learning and Data Analytics Approach

Heart disease is still a significant global public health concern, making it necessary to create precise prediction models to identify those who are at risk. The study suggests using data analytics and an advanced learning model (ALM) to forecast cardiac disease. The proposed approach uses cutting-edge data analytics methods and advanced learning algorithms to examine large datasets with various patient demographics, clinical characteristics, and medical histories. The training model Random Forest (RF) was used to find the significant patterns over the training phase. The data are preprocessed to clean and normalize the dataset, taking care of outliers and missing values. Following this, feature selection techniques are used to determine which significant predictors contribute to the risk of heart disease. Lastly, the suggested way to ALM is the combination of Support Vector Machine (SVM) and Neuro-Fuzzy Systems. Lastly, sophisticated data analytics approaches, including ensemble learning, hyper-parameter tweaking, and cross-validation, are used to improve the predictive models' generalization and resilience. Comprehensive tests are carried out using benchmark heart disease datasets, such as the Cleveland Heart Disease dataset and the Framingham Heart Study dataset, to validate the efficacy of the technique. The experimental results show that the suggested approach outperforms baseline models, resulting in higher precision and predictive capacity.