Predicting Mental Disorder in Social Network Using Machine learning

Predicting Mental Disorder in Social Network Using Machine learning

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CHAPTER-II: Literature Review

2.1 Introduction

Using data mining and machine learning approaches to forecast disease using patient treatment history and health data has been a battle for decades. Many studies have used data mining techniques to analyse pathological data or medical profiles in order to forecast specific diseases (Mohan et al., 2019). These methods aimed to predict disease recurrence. Furthermore, several systems attempt to forecast disease control and progression. The recent success of deep learning in a variety of machine learning applications has prompted a trend toward machine learning models that can learn rich, hierarchical representations of raw data with less pre-processing and provide more accurate results (Uddin et al., 2019).

With the advancement of big data technology, disease prediction has received more attention from the standpoint of big data analysis; various studies have been conducted by selecting characteristics automatically from a large number of data to improve the accuracy of risk classification rather than the previously selected characteristics to improve the accuracy of risk classification (Wu et al., 2019). The primary goal is to employ machine learning in healthcare to supplement patient care and improve outcomes. Machine learning has simplified the process of effectively diagnosing and identifying various diseases. Predictive analysis using efficient multiple machine learning algorithms aids in more accurate disease prediction and treatment of patients (Rastegar et al., 2019).

2.1.1 Machine learning in healthcare

The healthcare industry generates vast volumes of data on a regular basis that can be utilised to extract information for predicting sickness that may occur in the future for a patient based on their treatment history and health data. This concealed information in healthcare data will be used to make affective health decisions for patients in the future. In addition, by utilising informative data in healthcare, these areas require development. In the realm of healthcare, machine learning algorithms are used in a variety of ways. Medical facilities must be improved in order to make better decisions about patient diagnosis and treatment alternatives (Khourdifi & Bahaj, 2019).

In healthcare, machine learning assists people in processing large and complicated medical datasets and analysing them for clinical insights. This can then be used by doctors to provide medical care. As a result, when machine learning is used in healthcare, patient happiness can improve. The k-mean algorithm is used to predict diseases based on the patient's medical history and health information (Ho et al., 2019). It's crucial to know how to make a correct diagnosis of a patient through clinical examination and evaluation. Decision assistance systems that rely on computers may become vital in making compelling decisions. The health-care industry generates a great deal of data regarding clinical evaluations, patient reports, cures, follow-up appointments, medicine, and so on. It takes a lot of planning to get everything just right (Qu et al., 2019).

The quality of the data association has been harmed as a result of poor data management. Increased data volume necessitates a legitimate method of concentrating and processing information in a viable and efficient manner. To build a classifier that can segregate data depending on their properties, one of the various machine learning software is used. The data set is divided into two or more classes (Cho et al., 2019). These classifiers are used in medical data analysis and disease prediction. Machine learning is now ubiquitous, to the point where it is possible to utilise it on a daily basis without even realising it. It classifies both organised and unstructured data from a hospital. While other machine learning algorithms only deal with structured data and take a long time to compute, they are also inefficient because they keep all of the data as a training dataset and employ a sophisticated calculating procedure (Alaa et al., 2019).

Healthcare has always been an information industry. Healthcare providers and insurers have no shortage of variables to track because there are so many moving elements. The information gathered can be used for a variety of purposes. They keep track of costs and invoices. They keep track of what happens in hospitals and outpatient clinics. Importantly, the data document people's health on a microscopic and macroscopic level (Yahyaoui et al., 2019). It's difficult to overestimate the value of data in healthcare, particularly when it comes to enhancing healthcare systems. Although we focus on using medical data in disease prediction in this paper, there are many other aspects of healthcare that can be improved and even revolutionised by judicious data use (Rustam et al., 2020).

Getting access to healthcare data can be a difficult process. Before data can be shared, a number of obstacles must be overcome, including privacy rules and corporate considerations. Unfortunately, this can stymie the work of independent researchers who aren't connected to insurance firms or hospital systems. Developing academic partnerships with healthcare and insurance companies is an important step toward gaining access to data (An etal., 2020). This was something the author of the current work learned firsthand, and his adviser even more so. Despite these challenges, the benefits of better understanding and utilising medical data to enhance healthcare greatly outweigh the data access problems. Machine learning is the study of mathematical, computational, and statistical methods for detecting patterns in data and extracting information from it (Kannan & Vasanthi, 2019).

Data, on the other hand, are the physical expressions of the structures and processes that shape our environment. The goal of machine learning research is to develop technology that can solve previously intractable problems and alter human lives in a variety of ways. Many times, this has been realised to great effect. Healthcare is a promising area for machine learning since it is teeming with rich data and complex problems (Dinh et al., 2019). Machine learning is playing an increasingly important role in healthcare. Papers on a variety of machine learning approaches applied to healthcare challenges are being published in both computer science and healthcare publications, ranging from topic modelling and natural language processing to Markov processes and hidden Markov models, and so on (Zheng et al., 2019).

Obviously, the goal of machine learning research in healthcare is not to replace human doctors and nurses, but to supplement and help them where they suffer. Machine learning approaches can improve the quality and consistency of care on a big scale by doing exactly what humans can't, analysing massive amounts of data quickly (Raita et al., 2019). Furthermore, machine learning has the potential to enhance more basic research in healthrelated domains including automated drug development, genomics, and computational biology. Despite the confidence expressed in the preceding paragraphs, machine learning's success in healthcare is not assured. Researchers must stay anchored in the field, ask the proper questions, and always critically assess what they do, rather than getting caught up in the jargon and hype. We look at two key considerations to bear in mind when practising machine (Ganggayah et al., 2019).

2.1.2 Prediction of human disease using ML

ML algorithms were first built and used to analyse medical data sets. ML now recommends a variety of tools for effective data analysis. The digital revolution, particularly in the last few years, has made data collecting and storage more affordable and accessible. Data collecting and examination machines are being installed in new and modern hospitals to enable them to collect and share data in large information systems (Mezzatesta et al., 2019).

For the study of medical data, machine learning technologies are particularly successful, and a lot of progress has been made in terms of diagnosing disorders. In modern hospitals, accurate diagnostic data is displayed in the form of a medical record or reports, or in their own data section (Abdar et al., 2019).

A valid diagnostic patient record is loaded into a computer as an input to execute an algorithm. Results can be derived automatically from previously solved situations. This generated classifier aids physicians in diagnosing new patients at a high rate and with greater accuracy (Heo et al., 2019). Non-specialists or students can be taught to utilise these classifiers to diagnose problems. In the past, machine learning has provided self-driving automobiles, speech recognition, quick web search, and better human vision. Machine learning is now so pervasive that it is possible to utilise it numerous times a day without even realising it. Many researchers think it's a great method to get closer to the human level (Cheng et al., 2020).

Machine learning algorithms are used to find high-dimensional patterns and many data sets in electronic health records. The theme of ML is pattern recognition, which can help forecast and make judgments about diagnosis and treatment. Machine learning algorithms are capable of handling large amounts of data, combining data from several sources, and incorporating background information into a research (Ngiam & Khor, 2019).

2.2 Related Study

Due to rising computer power and the availability of massive datasets via open-source technologies, machine learning is now frequently used in today's world. The model's quality of transmission (QoT) can provide some information. An attempt has been made to monitor the QoT in order to determine the model's physical status (Sartzetakis et al., 2019). In an another study, the application of machine learning in intrusion detection systems was attempted (Otoum et al., 2019; Ali et al., 2019; Mishra et al., 2019). For the extreme learning machine, the author Song et al. (2019) offered a modified optimization strategy. A supervised deep-learning technique is utilised to diagnose defects in the induction machine system in another investigation (Razavi-Far et al., 2019). In visible light communication systems, (Ma et al., 2019a) ML is also employed in demodulation techniques. Another topic where ML has been used to achieve optimal performance in wireless networking is resource management (Shen et al., 2020).

Another study Wang et al., (2019) proposes a method for achieving local and global updates, which is crucial for the learning process. ML is also used to overcome problems with wireless networks. Chen, Challita, Saad, Yin and Debbah (2019) demonstrated how artificial neural networks (ANNs) may be utilised to solve a variety of wireless network difficulties. Nawaz, Sharma, Wyne, Patwary and Asaduzzaman (2019) proceeded over the many models that are employed in 5G technology in great depth. In another study (de Lima et al., 2019), detailed research on how neuromorphic photonics systems are used to solve MLbased challenges is offered in this work. Artificial neural networks have been used in the endeavour to detect falls and daily activity (Chelli & Patzold, 2019). There has even been an attempt to diagnose ML models (Zhang et al., 2019). In addition, machine learning is utilised to detect viruses in Android software (Ma et al., 2019b).

Tang et al. (2020) provided insight into the application of machine learning in vehicle 6G networks In addition, ML is used to predict flight delays (Zhang et al., 2019). Another approach is to utilise ML to determine the dynamics of proteins (Noé et al., 2020). Another study Zhu et al. (2020) examined at the use of artificial intelligence in wireless communication, and a new research area called edge learning was formed. Machine learning models are now being used more frequently in healthcare. Machine learning models are utilised for early disease prediction because of their capacity to extract meaning from data and predict outcomes. In order to find solutions to complicated problems, machine learning is applied in heart disease research. For example, some data mining techniques (Patel et al., 2015) are used to analyse heart disease data in order to find patterns and aid in heart disease prediction. In another study Haq et al. (2018a), for the diagnosis of cardiac disease, a hybrid of machine learning models has been proposed.

2.2.1 Studies related to disease prediction

For heart disease prediction, the authors Khourdifi and Bahaj (2019) used numerous machine learning models and implemented various optimizations, including particle swarm optimization (PSO) paired with ant colony optimization (ACO). In a study, Latha and Jeeva (2019) for the prediction of cardiac disease, an ensemble technique is applied. The ensemble technique improved the accuracy of weak classifiers, according to their findings. Several research have attempted to correlate cardiac disease and coronavirus to see if there is a link between the two (Singh & Kumar, 2019a; Kaur et al., 2019; Stephen et al., 2019; Pathak et al., 2020; Shukla et al., 2020; Teng et al., 2019). Several attempts have been made to detect cardiac disease and prevent it from causing major injury before it occurs (Wiens, 2019; Zhang & An, 2017; Kokubo et al., 2020; Suls et al., 2020; Sattar et al., 2020). Machine learning models are also utilised to solve problems in the medical area with data, such as coronavirus illness. Machine learning techniques and mathematical models (Jia et al., 2020), for example, are used to estimate the number of persons infected with the coronavirus and the time it will take for the virus to be eradicated in China

In a study conducted by Bullock et al. (2020), wide range of applications, tools, and datasets to investigate how artificial intelligence can be used to combat coronavirus. The significance of artificial intelligence and machine learning in the fight against coronavirus is critical since it will aid in early coronavirus prediction (Allam et al., 2020). It is vital to characterise the spread of information on social media in the case of a coronavirus outbreak (Li et al., 2020). Coronavirus infection is more common in patients with hypertension, diabetes (Fang et al., 2020), and advanced age (Rothan & Byrareddy, 2020). A study was conducted to see if there was a link between coronavirus and diabetes (Hussain et al., 2020; Muniyappa & Gubbi, 2020; Ma & Holt, 2020; Fadini et al., 2020). Diabetes has been around for a long time in civilization. Diabetes is also influenced by a person's body, diet, and way of life (Olokoba et al., 2012).

In diabetic challenges, machine learning models are utilised to find answers and provide early prediction utilising machine learning models. For instance, forecasting incident diabetes, the authors Alghamdi et al. (2017) built an ensemble-based model. There are 32,555 patients in the database used in the study. In another study, machine learning models were used to predict prediabetes in the Korean population (Choi et al., 2014). Sneha and Gangil (2019) evaluated the data to find the best features for diabetes early detection. To predict and diagnose diabetes, the authors Zou et al. (2018) used a dataset from Luzhou, China. Diabetes has been attempted to be identified in developing countries (Misra et al., 2019). Because older people are more likely to develop diabetes than younger people, an attempt has been made to compile a list of clinical procedures for dealing with diabetes in the elderly (LeRoith et al., 2019). Table 1 shows the results of a comparison of the existing techniques.

Table 1: Comparative analysis of the existing techniques.

Author Name & Year Applicationr Features Model
Patel et al. (2016) Heart disease Decision tree shows better results as compared with J48, logistic model tree algorithm, and random forest Data mining technique
Davenport and Kalakota (2019) Healthcare AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period Artificial intelligence in healthcare
Keto et al. (2016) Open heart In addition to individual CVD risk factors, Framingham and systematic coronary risk evaluation (SCORE) algorithms were used to assess the absolute risk of a CVD
Otoum et al. (2019) Wireless networks A Comprehensive analysis was presented on the use of machine and deep learning for IDS systems in wireless sensor networks (WSNs) Deep learning
SideyGibbons and SideyGibbons (2019) Medicine Used general linear model (GLM) regression, support vector machines (SVMs) with a radial basis function kernel, and singlelayer artificial neural networks Machine learning models
Khourdifi and Bahaj (2019) Heart disease Artificial neural network optimized by particle swarm optimization (PSO) combined with ant colony optimization (ACO) approaches Machine learning
Singh and Kumar (2019a) COVID-19 Used to detect and diagnose COVID-19. Chest X-rays is preferred over CTscan Deep transfer learning
Stephen et al. (2019) CO VID-19 Abscesses, and enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized Deep convolutional neural networks Computed tomography (CT) scans to diagnose pneumonia, lung inflammation
Haq et al. (2018b) Medical The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers Heart disease
Singh and Kumar (2019b) COVID-19 A DenseNet201-based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not, i.e., COVID-19 (+) or COVID (−) DenseNet201

2.3. Summary

Accurate medical data analysis aids early disease detection, patient care, and community services as a result of big data advances in the biomedical and healthcare sectors. When medical data is of inferior quality, the precision of the analysis decreases. The stated comparison intends to clear the gap for the various disease and predictions, making it easier for each prediction methods to achieve the higher accuracy. This may be used to support a variety of different Machine Learning algorithms for disease prediction and many systems strategy relies exclusively on automating the prediction method.

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