The technology boom in Industry 4.0 has resulted in the generation of large volume of medical data. Smart healthcare systems have burgeoned with the convergence of blockchain, machine learning, and internet of things (IoT). The amalgamation of privacy-preserving blockchain technology and predictive diagnosis by machine learning models produces an enhanced healthcare system. This chapter aims to shed light on the machine learning algorithms and blockchain technologies currently used in the healthcare industry. The advantages of opting for blockchain for preserving data integrity and privacy are highlighted. A few applications and steps involved in implementing blockchain and machine learning in healthcare, as well as the challenges faced while doing so, are discussed. Finally, a theoretical blockchain-machine learning internet of medical things(IoMT) framework is proposed, drawing from the recent research advancements.
TopIntroduction
Role of Healthcare in E-Governance
The past decade has witnessed a tremendous growth of data and several research advancements to handle the influx of information. Countries have invested thousands of dollars in information technology to build and efficient system for storage, structuring and processing of big data, for efficient governance. Thus, digitalization begat the inception of E-governance. E-governance is the use of Information and Communication Technology (ICT) for enabling government services, transfer of information, communication transactions across different organizations. Supply Chain Logistics, elections and finance are a few among the multitude of e-governance initiatives. One prominent but challenging sector is the healthcare industry.
Machine Learning
Machine Learning (ML) is a subset of Artificial Intelligence (AI) which enables computers to emulate human thinking pattern, without explicitly being programmed. Its definition and origin dates to 1959 and was popularized by the Pioneer in the field of Artificial intelligence and the famous inventor of the Checkers self-playing program. Tom Michell defines machine learning functionally as “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”
To define machine learning in layman’s terms, it is basically the ability to make computers think like humans. For example, when shooting a basketball, the first attempt results in the ball lands a few inches from the backboard. After altering the shooting stance, the ball bounces off the backboard. Eventually after adjusting the shooting force, position, zoning in on the target, the ball falls in the hoop. Thus, the result improves by learning from each trial. Replicating similar cognitive thinking processes in computers is what machine learning aims to do, and Alan Turing brings the question “can machines do what we (as thinking entities) can do?” to light in his paper “Computing Machinery and Intelligence”.
Machine learning is commercially applied in forecasting the weather, detecting fraudulent transactions, and predicting the remaining useful life of industrial machinery with the aid of statistical algorithms and machine learning models. For example, the temperature, humidity, rainfall data collected over the past few months is fed into the machine learning model. The model then identifies the hidden pattern through correlation analysis, feature extraction and so on to predict if it will be sunny tomorrow. The more convoluted and well-known implementation of this use case is the Google weather forecast.
The field of data analytics, where machine learning techniques are used for predicting the business outcome, based on historical data is known as predictive analytics. Data scientists, engineers, and business analysts unveil the hidden patterns and produce reliable insights about the future trends and events, to facilitate businesses.