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Machine learning is the technology of a new era, and it is the field that is used to construct models and is helpful in prediction of diseases. Machine learning algorithms are used to identify hidden patterns and relationships in historical data. Data are needed to support medical decision-making to predict accurate, robust, and efficient models. The use of machine learning in modern healthcare systems is increasing and necessary [7]. By 2025, CAGR has raised machine learning targets in the healthcare sector from $2.1 billion to $50.2% in 2018 to $36.1 billion. In fact, machine learning has an important part of patient data compared to improving healthcare delivery systems, cutting costs and developing, and monitoring and handling treatment processes and medicines. As we all know that maintaining and updating and recording the patient’s medical history is a very expensive process. These problems are solved by the use of machine learning technologies to reduce time, effort, and money.

Figure 3.3 Machine learning life cycle model.

To build an efficient machine learning project in healthcare, there are various steps to do such as data gathering, data wrangling, analyze data, train the model, test the model, and deployment, as shown in Figure 3.3. Sickness treatment has ordinary influence for healthcare physicians, and impeccable diagnosis at the right time is very important for a patient [2]. Compared to the previous approach, machine learning first builds the model and then presents the first reliable and accurate predictions for model construction without defining patient characteristics.

There are various machine learning algorithms for thyroid detection, some of which are as follows.