A Modified Approach of Early Prediction of Heart Disease Using Advanced Kernel Support Vector Machine with Genetic Algorithm

Abstract

These days, heart disease may be a leading cause of death and despair. Deaths from heart disease are rising faster than those from other diseases everywhere in the world. It is quite difficult to foresee the likely consequences related to heart disease in advance. Several techniques are developed that use clinical knowledge sets for identifications in order to identify the likely problems. Several of the methods forecast risk variables associated with heart conditions. Numerous obvious risk indicators that are prevalent in people with heart conditions will be successfully employed for identification. In addition to aiding medical examiners in making predictions, system-supported risk factors also alert patients in advance of the likely existence of heart conditions. These techniques are helpful in preventing time and money waste. In light of this, a genetic algorithm is used in conjunction with a support vector machine, which is mostly based on intelligent systems, for identification purposes in this investigation. To determine what kind of cardiac issue a patient may have, whether or not it is a heart attack, clinical symptoms backed by a knowledgeable system are used. The genetic algorithm-based support vector machine is mostly used to analyze patient knowledge in India. It has been suggested to use the Advanced Kernel Support Vector Machine (AKSVM) technique to repeatedly and incredibly quickly confirm the support vectors. Genetic algorithms, which also optimize SVM's classification accuracy, are used to choose the relevant and essential options and eliminate the unnecessary and redundant ones. The findings indicate that, in contrast to current techniques, support vector machines will be successfully used for the identification of cardiac conditions. The results highlight the importance of accurate identification and the advantages of knowledge coaching for machine learning-based autonomous diagnosis systems.