Mayuri Mehta - Tracking and Preventing Diseases with Artificial Intelligence

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All around the world, the spread of infectious diseases is a major concern as it directly impacts the health of people. While some infectious diseases may have a minor impact on society, some can have major impacts such as the recent SARS-CoV-2 coronavirus pandemic, also known as COVID-19.

To cope with the spread of infectious diseases, some traditional approaches are used such as to study the effect of medicines and develop new ones, design appropriate vaccines, and enforce various measures such as washing hands, wearing face masks, and doing temperature checks. However, despite the usage of such measures and medical advancements, there remain several incurable diseases for which prevention is the only cure. Besides, time is often critical when coping with new diseases that are highly contagious such as COVID-19 as no vaccine or very effective medicine is initially available.

To cope with these challenges, artificial intelligence (AI) has been rapidly adopted to assist physicians in diagnosis, disease tracking, prevention, and control. Due to increasing the availability of electronic healthcare data and rapid progress of analytics techniques, a lot of research is being carried out in this area by applying machine learning and data mining techniques to assist the medical professionals for making a preliminary evaluation.

This book is a collection of 11 chapters that provides a timely overview of recent advances in this area, that is, to use artificial intelligence techniques for tracking and preventing diseases. The target audience of this book is researchers, practitioners, and students. A brief description of each chapter is given below.

In Chap. , four approaches to identify stress by recognizing the emotional state of a person have been proposed. Pradeep et al. have analyzed the performance of the proposed approaches using Surrey Audio-Visual Expressed Emotion (SAVEE) and ENTERFACE databases. The results illustrate the considerable reduction in computational time and show that vector quantization-based features perform better than mel-frequency cepstral coefficients feature.

In Chap. , Fayemiwo et al. compared various approaches for the detection of COVID-19 from X-ray images. The problem is viewed as a classification problem with two classes (normal vs COVID-19) or three classes (normal, pneumonia, and COVID-19). A fine-tuned VGG-19 convolutional neural network with deep transfer learning shows that high accuracy can be obtained (from 89% to 99% depending on the scenario.

In Chap. , Falguni et al. aim to develop an intelligent diagnostic system for glaucomaan eye-related disease, from the data obtained through clinicians by various examination devices or equipment used in ophthalmology. The classification is done by using a hybrid approach using artificial neural network, Nave Bayes algorithms, decision tree algorithms, and 18 medical examination parameters for a patient. FGLAUC-99 is developed with J48, Nave Bayes, and MLP classifiers with accuracy of 99.18%. The accuracy is not compared with other classifiers as the dataset is exclusively developed.