Heart Condition Forecasting Using High-Accuracy ANN Algorithms

Increasing life expectancy has many challenges like day-to-day illness, chronic diseases, allergies, and cardiac arrest. In reference to technical science, there are many wearable devices to measure basic health parameters but their accuracy and precision are also a challenge. The heart plays a pivotal role in the human body so measuring its heartbeat accuracy and precision is the highest priority because little mistakes and manipulation can cause severe illness, fatigue, and death of a person. In this paper, our major aim is to improvise the heart disease prediction by using the best possible algorithms of high accuracy. To predict cardiac arrest there are different technologies used such as artificial intelligence (AI) and machine learning (ML), and to improve accuracy and precision we took the combination of four most efficient algorithms, i.e., K-nearest neighbors, light gradient boosting machine, support vector machine, and decision tree classifier. This research paper throws light on the improvision done with different algorithms for predicting the probability of heart diseases, and after processing our dataset, we came to know that SVM and KNN are the two best of all algorithms that can ever predict the result with the highest accuracy.