The Identification of Health of Heart Through Predictive ML Based Technique in Integration with Existing System

New classes of diseases include; cardiovascular diseases: These are diseases associated with the heart and blood vessels and they are some of the classes of emerging infectious diseases that threaten human health around the world. for public health, is a big blow to complicated the issue that this is becoming a problem gradually, many researchers have been developing complicated systems that integrate patient’s electronic health records, as well as many others, and artificial neural networks algorithms in a bid to produce high diagnostic results. most of the frequent artificial intelligence particularly in a context where full patient data are instantiated for the purpose of inferring the heart diseases. and thereby merely claiming the most primordial facets of our population’s health. describes with exemplary accuracy the use of four separate classification methods: The used models in the current study are a) Multilayer Perceptron (MLP) b) RandomForest (RF) c) Support Vector Machine (SVM), and d) XGBoost. Before the development of the conceptual models, this study went through pre-processing and doing features extraction to these analyses These were done in line with what is regarded as the best approach when doing the last phase in a given research process. When evaluating the results, the following standards were used: accuracy, precision, recall, F1-score as it has been mentioned earlier, the XGBoost model shows a very high level of effectiveness The accuracy rate is rather high, 88%, which means the effectiveness of the instrument is high. Thus, this research study will fill this research gap of knowledge, as mentioned earlier, in a way that is appreciable. is a clear step in the right direction in addressing the high error rate that is associated with cardiovascular diagnosis while at the same time enhancing the validity with the MLP as well as the SVM.