Heart disease diagnosis using optimized features of hybridized ALCSOGA algorithm and LSTM classifier

Cardiac disease is the predominant cause of global death mainly due to its hidden symptoms and late diagnosis. Hence, early detection is important to improve quality of life. Though traditional researches attempted to predict heart disease, most of them lacked with respect to accuracy. To solve this, the present study proposes a hybridized Ant Lion Crow Search Optimization Genetic Algorithm (ALCSOGA) to perform effective feature selection. This hybrid optimization encompasses Ant Lion, Crow Search and Genetic Algorithm. Ant lion algorithm determines the elite position. While, the Crow Search Algorithm utilizes the phenomenon of position and memory of each crow for evaluating the objective function. Both these algorithms are fed into Genetic Algorithm to improve the performance of feature selection process. Then, Stochastic Learning rate optimized Long Short Term Memory (LSTM) is proposed to classify the extracted optimized features. Finally, comparative analysis is performed in terms of accuracy, recall, F1-score, and precision. Moreover, statistical analysis is performed with respect to Sum of Squares (SS), degree of freedom (df), F Critical (F crit), F Statistics (F), p, and Mean Square (MS) value. Analytical results revealed the efficiency of proposed system over conventional methods and thereby confirming its efficiency for predicting heart disease.