5 A data-driven framework for future healthcare diagnosis through predictive analytics
-
Edeh Michael Onyema
,
Sharanya S
Abstract
The swift development of technological advances, along with the continuing explosion of healthcare data, has ushered in a new era of precision medicine and intelligent diagnostics. Traditional diagnostic procedures, which are frequently based on clinical skill and limited datasets, are increasingly being supplemented and, in some cases, transformed by advanced data-driven methodologies. Predictive analytics is at the center of this revolution, a powerful tool that uses historical and real-time data to forecast health outcomes, detect illnesses earlier, and tailor patient care. This paper outlines an extensive approach for using predictive analytics into future healthcare diagnostics based on machine learning algorithms, deep learning and real-time observation of patients. This approach intends to increase decision-making accuracy, reduce diagnostic mistakes, and hopefully enhance outcomes for patients. As medical systems throughout the world deal with increasing bills, elderly patients, and complex disease profiles, the shift to data-driven diagnostics will be more than just a novelty, but vital to quality healthcare service delivery.
Abstract
The swift development of technological advances, along with the continuing explosion of healthcare data, has ushered in a new era of precision medicine and intelligent diagnostics. Traditional diagnostic procedures, which are frequently based on clinical skill and limited datasets, are increasingly being supplemented and, in some cases, transformed by advanced data-driven methodologies. Predictive analytics is at the center of this revolution, a powerful tool that uses historical and real-time data to forecast health outcomes, detect illnesses earlier, and tailor patient care. This paper outlines an extensive approach for using predictive analytics into future healthcare diagnostics based on machine learning algorithms, deep learning and real-time observation of patients. This approach intends to increase decision-making accuracy, reduce diagnostic mistakes, and hopefully enhance outcomes for patients. As medical systems throughout the world deal with increasing bills, elderly patients, and complex disease profiles, the shift to data-driven diagnostics will be more than just a novelty, but vital to quality healthcare service delivery.
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
