An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

• Published in: Frontiers in public health Vol. 10; p. 860396
• Main Authors: Rashid, Junaid, Batool, Saba, Kim, Jungeun, Wasif Nisar, Muhammad, Hussain, Amir, Juneja, Sapna, Kushwaha, Riti
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 31.03.2022
• Subjects: Algorithms; Artificial Intelligence; artificial neural network (ANN); Chronic Disease; chronic diseases; feature selection; Humans; medical diagnosis; Neural Networks, Computer; prediction; Public Health; Support Vector Machine; medical diagnosis; prediction; artificial neural network (ANN); chronic diseases; feature selection
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2022.860396

Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.