Abstract
This chapter addresses how machine learning (ML) and artificial intelligence (AI) are revolutionizing the field of healthcare disease prediction. It describes how to employ public information and machine learning algorithms to forecast conditions including osteoarthritis, breast cancer, and Alzheimer's. Case studies from real-life scenarios show how predictive models work effectively for early diagnosis and customized responses. Critical analysis is done on ethical issues such as model interpretability and patient privacy. The chapter places a strong emphasis on the necessity of ethical frameworks and competent data handling to direct the incorporation of ML into healthcare. This chapter adds to the continuing discussion on AI's role in healthcare by providing a thorough analysis of disease prediction in osteoarthritis, Alzheimer's, and breast cancer, promoting better patient care and public health outcomes.
TopIntroduction
Daily living changes for humans, but health improves or deteriorates with each generation. There are never enough answers in life. Occasionally encounter a sizable population with fatal health conditions as a result of delayed disease discovery. Researchers and medical experts are working very hard to reduce the death rate from diseases because they are a global problem. In the medical industry, predictive analytic models have grown increasingly prominent in recent years. because of the volume of healthcare data originating from a variety of unreliable and incompatible data sources. But managing, storing, and analysing the massive amounts of historical data and the constant stream of data generated by healthcare services using ordinary database storage has become an unparalleled challenge.
Predictive analytics are crucial for the healthcare industry. It can have a major impact on the accuracy of disease prediction, which might potentially save patients' lives in the event of an early and correct prognosis; on the other hand, an inaccurate prediction could endanger patients' lives. Diseases must therefore be accurately assessed and predicted. Thus, reliable and efficient methods for predictive analysis in healthcare are required.
The area of advanced analytics known as “predictive analytics” is used to forecast future events that are not yet known. Predictive analytics use a variety of data mining, research, modelling, machine learning, and artificial intelligence (AI) methods to assess previous discoveries and estimate future occurrences. Because machine learning approaches perform very well in managing large-scale datasets with consistent properties and noisy data, they have gained popularity in predictive analytics. Studies using observational data demonstrate that machine learning is suitable for creating prediction models through the extraction of patterns from huge datasets.
Figure 1 below shows the several domains in which predictive analysis is beneficial. These models are widely used in predictive data analytics applications like document categorization, risk evaluation, pricing forecasting, and consumer behaviour prediction.
Figure 1. Predictive analytics in healthcare
Key Terms in this Chapter
Predictive Analysis: Predictive analytics is the phrase used to describe the application of modelling and statistical methods to forecast performance and future results. In order to ascertain if past and present data patterns are likely to recur, predictive analytics examines them.
Population Health Management: Within the healthcare sector, population health management (PHM) is a discipline that investigates and streamlines the provision of care to a group of people or the general population.
Alzheimer's Disease: Alzheimer’s disease is a neurodegenerative disease/ disorder that is progressive and is characterized by decline in cognitive activity, memory loss and behavioral changes.
Personalized Medicine: A new field of medicine known as “personalised medicine” gives suggestions concerning sickness prevention, diagnosis, and treatment based on an individual's genetic profile. Considering a patient's genetic makeup can help medical personnel select the best therapy or substance and administer it according to the proper schedule or dosage.
Ethical Considerations: Ethical considerations are defined as moral principle and values in the context of predictive analysis in healthcare. These address issues such as data security, patient privacy, and the equity of healthcare resources.
Disease Surveillance: The analysis and systematic monitoring/observance of healthcare related data to identify trends, patterns, and outbreaks of diseases within a population. Disease surveillance ensures to track the spread of disease, assess public health risks and guide preventive measures.
Breast Cancer: A prevalent cancer that forms in the cell of breast tissue which primarily affects women but can also happen in men.
Preventive Interventions: Any strategy or action aimed at a population or individual who is not currently experiencing any discomfort or disability as a result of alcohol or other substance use but has been identified as having a high risk of developing problems related to either their own use of alcohol or other substances or the use of alcohol or other substances by others is known as a preventive intervention.
Machine Learning: Machine learning is a branch of artificial intelligence, which is defined broadly as a machine's capacity to emulate intelligent human behaviour. Artificial intelligence systems do complex tasks in a manner similar to how humans solve problems.