INCL: A Robust Design of Artificial Intelligence Assisted Learning based Cardiovascular Disease Detection using Improved Neural Classification Logic

In both industrialized and developing nations, cardiovascular illnesses have surpassed all others as the leading killers in recent decades. One way to lower the mortality rate is to discover heart illnesses early and to continuously supervise physicians. Unfortunately, due to the increased intelligence, time, and skill required, reliable identification of cardiac illnesses in all situations and 24-hour medical consultations are not yet possible. To anticipate the onset of cardiac problems, this research suggested a preliminary architecture for a cloud-based system that would use machine learning algorithms. An effective machine learning method for cardiac illness identification has emerged from a unique examination of many machine learning algorithms in the open-source programming language Python. This paper details the development of an AI-powered system for detecting cardiac diseases, called Improved Neural Classification Logic (INCL). To test how well it works, it is cross-validated with the traditional model, Support Vector Machine (SVM). We offer data processing that comprises converting categorical columns and dealing with categorical variables. We outline the primary steps in developing an application, which include gathering databases, running logistic regression, and assessing the features of the dataset. The data analysis is necessary for the use of an Improved Neural Classification Logic method that is designed to detect cardiac problems more accurately; this algorithm is deemed noteworthy due to its around 95.67% accuracy rate over training data. We go on to talk about the tests and outcomes of the Improved Neural Classification Logic, which leads to more accurate study diagnosis.