Applications of machine learning techniques (ML) are having a significant impact on a number of expanding industries, including the healthcare sector. ML is a part of artificial intelligence (AI) technology with the primary objective to increase the efficiency and precision of medical professionals' work. AI presents a significant opportunity in the healthcare sector as most countries are now struggling with overburdened healthcare systems and a dearth of competent medical personnel. Healthcare data is expanding daily and can be utilised to choose the best study sample, gather additional data points, and assess participant data from ongoing studies. ML-based methods/algorithms are employed in the early detection and diagnosis of numerous diseases. For a very long time, early disease prediction and detection have been crucial areas of research for the diagnosis of all diseases. Machine‐learning (ML) algorithms have proven to be quite efficient in disease detection and decision making in healthcare.
TopIntroduction: What Is Machine Learning?
Machine Learning (ML) is a subset of artificial intelligence (AI), which is one of the most rapidly growing and utilised technical subjects in all over globe. Big data, also known as structured and unstructured data that has grown significantly, has made machine learning (ML) essential because it is impossible to handle this data using other approaches (Javaid et al., 2022). The field of machine learning is where computers are taught to mimic human behaviour. The utilisation of data and algorithms, which are composed of statical tools, is the focus. The correct processing of a huge number of data, creating a machine learning model, training it, and refining it to achieve high accuracy are all components of ML techniques (Islam et al., 2022).
The learning of machines or models in ML depends on the raw data so we can say that data set is a key component to make a proper model in ML. Data can be either structured or unstructured. Based on this data the ML model forecast a pattern regarding the data and uncover the hidden pattern if any. The accuracy is then calculated by comparing the prediction to the known answer, which is commonly known as output, i.e. the structured data. The model then attempts to locate known data points in order to increase accuracy even further. This is how the machine learning technique trains and produces models that assist the machine in learning human behaviour (Koptelov, 2022).
Machine Learning Working
When a machine learning system learns from historical data, it creates prediction models and predicts the results for new data as it comes in. The amount of data provided determines how accurate the anticipated results will be, and accuracy is a crucial element of a good model. In machine learning, it has been noted that a huge amount of data aids in the development of a better model that more precisely predicts the outcome. The machine learning algorithm working is explained in the following block diagram (Kumar et al., 2023):
Figure 1.
Working of machine learning algorithm
Types Of Machine Learning
Machine learning can be categorized in three types:
- 1.
Supervised learning
- 2.
Unsupervised learning
- 3.
Reinforcement learning
Figure 2.
Types of machine learning
One of the types of machine learning is supervised learning which is fully dependent on structured data. In supervised machine learning, we provide the machine learning system a sample set of structured data to train it, and then it makes predictions about the output based on the input. After training and processing, we test the model by supplying sample input data to see if it predicts the intended outcome or not. The system builds a model utilising structured data to interpret the datasets and learn about each data (Islam et al., 2022; Mitra et al., 2021).
In supervised learning, mapping input and output data is the main objective. The foundation of supervised learning is supervision, just like when a student is studying under the instructor's supervision.