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for the detection of tissue in radiotherapy. Outbreak of several chronic diseases can also be predicted using data gathered through satellite, online information and social media updates. Big Data provides a major part by maintaining the EHR which is in form of complex unstructured data. Another major challenging area is related to the infrastructure of the hospital. Moving toward more advance technologies, the infrastructure need to be updated which is time consuming and costly.

      Keywords: Machine learning, deep learning, healthcare, electronic medical records (EMRs), big data

1.1 Introduction

Machine Learning (ML) is a computer program that learns from experience with respect to a particular task assigned and gives result accordingly. The performance of such computational algorithm improves with experience. Health is a major area of concern for everyone and to provide the best healthcare service is becoming one of the major goals of almost every country. But doing that is not an easy task as collecting the medical data and providing it to leverage knowledge so that the best possible treatment can be provided is itself very challenging. So, data plays a crucial part in extracting information and addressing problems related to health. ML has the ability to extract information from the data being provided and further helps in resolving this fundamental issue to some extent.

      The huge medical data need to be interpreted and processed by epidemiologists. The input of healthcare providers has been expanded and also created new opportunities due to the availability of huge amount of data related to patients and facility being provided which will further help in achieving the necessary approaches related to prevention and treatment [1]. Due to the complexity of medical data and also lack of technology, the collection was completely ignored in the past. ML algorithm has proved to overcome such difficulties by collecting the medical data securely and further applying it for diagnosis and prognosis. ML has improved several domains like Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and computer vision by using the data. Creating the correct model for maintaining the electronic medical records (EMRs) is a challenging issue due to its availability, quality, and heterogeneity.

      Big data is going to play a major role in revolutionizing the healthcare services in the coming future by using algorithm to detect and treat diseases [2]. Its impact on the practice of medicine is fundamentally going to transform the physician ability to personalize care directly to the people. The way to achieve this goal is by collecting data through handheld and wearable devices. This data will be compared with the genetic profile of people and further used for decision-making. The vast medical data needs to be integrated and accessed intelligently to support better healthcare delivery. Big data can create new networks of sharing knowledge by measuring and monitoring processes digitally [3]. Data comparison will be easier which will facilitate streamlined workflows greater efficiencies and improved patient care. Systematic analysis of extensive data can help to detect patterns so that clinicians can provide treatment to individuals and project health outcomes. Digital networks can bring together partners and knowledge sharing delivering context relevant clinical information enables more holistic decision-making. Healthcare can only benefit from big data when it is made structured relevant smart and accessible.

Figure 1.1 shows the how ML and big data analytics plays an important role in different fields associated with healthcare services. There are five major modules associated with ML algorithm and their contribution. The physician unstructured data is provided to ML algorithm and, in return, gets better clinical decision support. Also, the radiologist provides data in form of MRI/images and receives diagnostics from ML. It provides the patient a better lifestyle advice and treatment option. Patients are complex module with different genetic back ground so the risks associated with them are different over time. The drug makers get patients medical records for development of necessary drugs. The clinical research and development module provides bio illustration to the algorithm and gets predictive analysis.

1.2 Machine Learning in Healthcare

      In recent years, artificial intelligence (AI) has shown tremendous growth in transforming every aspect of life due to its wide range of tools which help in decision-making by analyzing data and integrating information. In terms of technology, Al has stolen spotlight and its advancements are quicker than our prediction [4]. ML being a subset of AI is transforming the world and raising its importance for the society. ML is defined as the study of methods and tools which help in identifying patterns within data and make computer learn without being programmed explicitly. ML can further be used to extend our knowledge regarding current scenario as well as for future prediction by allowing program to learn through experience. It uses the concept of AI for data optimization. Analyzing the best model to make the machine intelligent for data explanation is the goal. We will be discussing here its development in the field of medicine.

Figure 1.1 Machine learning and big data analysis in healthcare.

Figure 1.2 shows the different areas where ML algorithm is playing a major role to provide better healthcare services. Applying such technology will help in proving personalized treatment which will improve the health condition of patients. Drug discovery and research will be highly benefited as the structured data will be available. Further support will be provided to clinical decision-making and early detection of diseases will be possible to make the services better for individual. The use of Deep Learning (DL) and neural network will be highly helpful in improving the imaging and diagnostic techniques. By proving all the essential services in medical, the fraud related to medical insurance will minimized to the least.

      ML can transform the healthcare services by making us better providers of correct medical facility at the patient level. We can gather information on how different environmental exposure and lifestyle will vary the symptoms of disease. The intervention and history will help us decide treatment and decision-making. We can further understand the health and disease trajectory which will help in prepare us before arrival of the pandemics in worst possible situation. The resources available to us can be utilized in more efficient way with reduced costs. Also, the public health policies can be transformed in a way benefiting the people.

Figure 1.2 Application of ML in healthcare.

1.3 Machine Learning Algorithms

Depending upon the problem and approach to be applied, it has been categorized into various types among which major application lie into supervised, unsupervised, semi-supervised, reinforcement, and DL. The various types and its contribution to healthcare sector are shown in Figure 1.3.

      1.3.1 Supervised Learning

      This ML algorithm works under supervision, i.e., machine is trained with data which is well labeled and helps the model to predict with the help of dataset. Furthermore, supervised learning is divided into classification and regression. When the resultant variable is categorical, i.e., with two or more classes (yes/no, true/false, disease/no disease), we make use of classification. Whereas, when the resultant variable is a real and uninterrupted value, the problem is regression, here, a change in one variable is linked with a change in other variable (e.g., weight based on height). Some common examples of supervised ML in medicine is to perform pattern recognition over selected set of diagnosis

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