Innovative Integrated Health Reviews

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Innovative Integrated Health Reviews

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Received: February 10, 2022 / Revised: March 8, 2022 / Accepted: March 10, 2022 / Published: March 15, 2022

Globally, there is a significant unmet need for effective diagnosis of various diseases. The complexity of different underlying disease mechanisms and symptoms in patient populations presents major challenges in the development of tools for early diagnosis and effective treatment. Machine learning (ML), an area of ​​artificial intelligence (AI), allows researchers, doctors and patients to address some of these questions. Based on relevant research, this review describes how machine learning (ML) has been used to aid in the early detection of several diseases. Initially, a bibliometric analysis of the publication is performed using data from the Scopus and Web of Science (WOS) databases. A bibliometric survey of 1216 publications was conducted to identify the most cited authors, nations, institutions, and articles. The review then summarizes recent trends and approaches in machine learning-based disease diagnosis (MLBDD), considering the following factors: algorithms, disease type, data quality, implementation, and evaluation metrics. Finally, in this paper, we highlight key findings and provide insight into future trends and opportunities in the field of MLBDD.

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Artificial neural networks; convolutional neural network; COVID-19; deep learning; deep neural networks; diabetes; disease diagnosis; heart disease; kidney disease; machine learning; reviewed

In the medical field, Artificial Intelligence (AI) mainly focuses on the development of algorithms and techniques to determine whether a system’s behavior is correct in diagnosing a disease. A medical diagnosis identifies a disease or condition that explains a person’s symptoms and signs. Typically, diagnostic information is gathered from the patient’s history and physical examination [1]. This is often difficult due to the fact that many signs and symptoms are limited and can only be diagnosed by trained health professionals. Therefore, countries that do not have enough health workers for their population, such as developing countries such as Bangladesh and India, face difficulties in providing adequate diagnostic procedures for a large patient population [2]. In addition, diagnostic procedures often require medical tests, which low-income people often find expensive and difficult to access.

Since humans are prone to error, it is not surprising that patients are likely to be overdiagnosed. In case of overdiagnosis, problems such as unnecessary treatment will arise, which will affect people’s health and economy [3]. According to a 2015 report by the National Academies of Sciences, Engineering, and Medicine, most people will encounter at least one diagnostic error during their lifetime [4]. A variety of factors can contribute to a misdiagnosis, such as:

Machine learning (ML) is used almost everywhere, from cutting-edge technology (such as mobile phones, computers and robotics) to healthcare (such as disease diagnosis, security). ML is becoming increasingly popular in various fields, such as disease diagnosis in healthcare. Many researchers and practitioners point to the promise of machine learning-based disease diagnosis (MLBDD), which is inexpensive and time-efficient [5]. Conventional diagnostic procedures are expensive, time-consuming and often require human intervention. While human capabilities limit traditional diagnostic techniques, ML-based systems do not have these limitations, and machines do not perform as well as humans. As a result, a method can be developed to diagnose diseases with the sudden occurrence of a large number of patients in health care. To build the MLBDD system, healthcare data such as images (ie, X-ray, MRI) and tabular data (ie, patient status, age and gender) are used [6].

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Machine learning (ML) is a subset of AI that uses data as an input source [7]. Using predefined mathematical functions produces a result (classification or regression) that is often difficult for humans to achieve. For example, by using ML, it is often much easier to detect malignant cells in a microscopic image, which is usually difficult to do by looking at the images. Furthermore, following the advancement of deep learning (a form of machine learning), recent studies show an accuracy of MLBDD greater than 90% [5]. Alzheimer’s disease, heart failure, breast cancer and pneumonia are some of the diseases that can be diagnosed with ML. The emergence of machine learning (ML) algorithms in the field of disease diagnosis shows the utility of the technology in the medical field.

Recent findings in ML issues, such as unbalanced data in ML in the medical field, ML interpretation, and ethics, are just some of the many challenging areas that should be briefly addressed [8]. In this paper, we provide a review to highlight the emerging use of ML and DL in disease diagnosis and an overview of developments in this field to shed some light on current trends, approaches and issues related to ML in disease diagnosis. Provide brief information. . We begin by describing several machine learning methods and deep learning techniques with specific frameworks for detecting and classifying different forms of disease diagnosis.

The aim of this review is to provide recent and future researchers and practitioners in machine learning-based disease diagnosis (MLBDD) with guidance and enable them to select the most appropriate and optimal machine learning/deep learning methods. , thereby increasing the likelihood of rapidity. and reliable disease detection and classification in diagnosis. In addition, the review aimed to identify prospective studies related to MLBDD. In general, this study aims to provide adequate explanations for the following questions:

What diseases are researchers and practitioners particularly interested in when evaluating data-driven machine learning approaches?

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What machine learning and deep learning methods are currently used in healthcare to classify different forms of disease?

In this paper, we summarize several machine learning (ML) and deep learning (DL) methods that are used in various disease diagnosis applications. The rest of the paper is structured as follows. In Section 2, we discuss the background and overview of ML and DL, while in Section 3 we detail the article selection technique. Section 4 contains bibliometric analysis. In Section 5, we discuss the application of machine learning in various disease diagnoses, and in Section 6, we identify the most commonly used ML methods and data types based on related research. In Section 7, we discuss the results, possible trends, and issues. Finally, Section 9 concludes the article with a general conclusion.

Machine learning (ML) is an approach that analyzes data patterns to make key inferences using mathematical and statistical approaches, allowing machines to learn without programming. Arthur Samuel pioneered machine learning for games and pattern recognition algorithms for learning from experience in 1959, which was the first time significant progress was recognized. The basic principle of ML is to learn from data to make predictions or make decisions based on a given task [9]. Thanks to machine learning (ML) technology, many time-consuming tasks can now be completed quickly with much less effort. With the exponential expansion of computing power and data capacity, it has become easier to train data-driven ML models to predict outcomes with near-perfect accuracy. Several papers present different types of ML approaches [10, 11].

ML algorithms are generally classified into three categories such as supervised, unsupervised and semi-supervised [10]. However, ML algorithms can be divided into several subgroups based on different learning methods, as shown in Figure 1 . Some popular ML algorithms include linear regression, logistic regression, support vector machine (SVM), random forest (RF), and naive base (NB) [10].

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The decision tree (DT) algorithm follows the rule of divide and conquer. In the DT model, an attribute can take different values ​​known as a classification tree; Leaves represent different categories, while branches represent combinations of features that result in those category labels. On the other hand, DT can take continuous variables called regression line. C4.5 and EC4.5 are two well-known and most widely used DT algorithms [12]. DT is used in the following reference literature: [13, 14, 15, 16].

For classification and regression challenges, support vector machine (SVM) is a popular ML approach. SVM was introduced by Wapnik at the end of the twentieth century [17]. In addition to disease diagnosis, SVM has been widely used

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