Research Article
A Review of Hybrid Intelligent System For Diagnosis And Prediction of Heart Disease
- By Yusuf M, Hajara I.O - 13 Jul 2024
- Journal of Agricultural and Food Chemical Engineering, Volume: 4, Issue: 2, Pages: 1 - 8
- https://doi.org/10.58612/jafce421
- Received: March 29, 2024; Accepted: June 20, 2024; Published: July 13, 2024
Abstract
According to World Heart Federation (2016), heart disease is responsible for nearly 30% of the global deaths annually, therefore a leading cause of death. The European Society of Cardiology (2019) had stated that nearly half of the heart disease patients die within initial two years. Machine Learning Algorithms employ a variety of statistical, probabilistic and optimization methods to learn from past experience and detect useful patterns from large, unstructured and complex datasets. Recent research focuses on the disease risk prediction models involving Machine Learning Algorithms (e.g., support vector machine, logistic regression and artificial neural network), Specifically – supervised Learning Algorithms. Models based on these algorithms use labelled training data of patients for training. Given the growing applicability and effectiveness of supervised machine learning algorithms on predictive disease modelling, the breadth of research still seems progressing. This review employs about 40 research works that considers Machine Learning Algorithms, Data mining and hybrid intelligent system/approach for heart disease prediction in medical dataset involving multiple inputs. Most of these researches have shown that a significant progress has been made in classification related areas of Neural Networks, a detailed survey of their studies based on accuracy, specificity and sensitivity shows a hybrid system of Genetic Algorithm and Neural Networks (ANN-based methods) have been widely adopted in medical diagnosis due to their capability in handling complex linear and non-linear problems.