An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction - Rashid, Junaid; Batool, Saba; Kim, Jungeun; Wasif Nisar, Muhammad; Hussain, Amir; Juneja, Sapna; Kushwaha, Riti (Frontiers in Public Health, 2022)
Chronic diseases are rapidly growing and caused many deaths in past decades. Early diagnosis of chronic diseases has become an important research area in the medical field to enhance patient survival rates. Several research studies proposed classification approaches for specific disease detection. In this research paper, we proposed a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to detect five chronic diseases such as cancer, diabetes, heart, kidney, and hepatitis. Seven classification algorithms are compared to evaluate the proposed model prediction performance. The artificial neural network prediction model built with the PSO feature extraction approach outperforms the other classification approaches when evaluated with accuracy and F-measure. The proposed approach gave the highest F-measure of 99.67%. PSO shows an increase in F-measure up to 50%, which is remarkable. However, the classification model performance depends on the attributes of the data used for classification. The proposed approach results are compared on various chronic disease datasets, and its results are better than other state-of-the-art approaches. In addition, the artificial neural network processing required less time as compared to the random forest (RF) and support vector machine (SVM). This research study will be beneficial for the early diagnosis of chronic diseases in hospitals and online diagnosis systems.