Machine Learning Techniques For Prediction Research Articles

Analyzing the Performance of Machine Learning Techniques in Disease Prediction

The history of data stored can be used to forecast potential patterns and help companies make competitive decisions to increase their success and benefits. Many analysts look at healthcare sector data to identify and forecast illnesses in order to benefit patients and physicians in a variety of ways. This study is concerned with the diagnosis and estimation of heart disease. Heart disease is one of the most dangerous illnesses for humans, leading to death all over the world. Many different groups of researchers have used knowledge exploration methods in diverse fields to forecast heart disease and have shown acceptable degrees of precision. There were no real-time methods for analyzing and forecasting heart disease in its early stages. For the prediction of heart disease, decision trees are used to analyze various training and evaluation datasets. Classification algorithms such as Naive Bayes, ID3, C4.5, and SVM are being investigated. The UCI machinery heart disease data set is used in experimental studies.

A Study on Implementation of Machine Learning Techniques for Prediction of COVID-19

Abstract: The COVID-19 respiratory illness is that the name for the Corona virus and could be a kind of SARS-cov-2 virus. Machine Learning strategies will play a key role in predicting the unfold of infection with the assistance of predictive analytics. Machine Learning techniques facilitate mine knowledge to raised estimate and predict the COVID-19 infection standing. A Fine- tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine learning techniques has been planned for various states of India. Many enterprises need to embrace and customize artificial intelligence (AI)–based technology so as to deal with the issues display by the COVID-19 pandemic. Artificial intelligence (AI)– based technologies have compete a vital role in coping with difficult challenges, and various companies are fast to adopt and tailor these technologies. A scientific survey of the literature on the role of artificial intelligence (AI) as an entire and productive technology within the fight against the COVID-19 drawback within the disciplines of medical specialty, diagnostics, and ill health progression was the most important purpose of this inquiry. As a part of this systematic review, we tend to gathered publications from this COVID-19 analysis that created use of by artificial means intelligent approaches to produce insights into numerous COVID-19 themes. Our findings highlight to vital factors, data types, and COVID-19 resources that can be accustomed facilitate analysis and clinical analysis and may be used to inform future investigation. Keywords: COVID-19, Corona virus, SARS-CoV2, Artificial intelligence, Machine learning, Deep learning, Systematic review, Epidemiology, Pandemic, Neural Network

Machine learning techniques for prediction of recurrence after curative resection in the patients with rectal cancer

Background: The patients who have rectal cancer without distant metastases are usually treated with radical surgery. Several factors can affect tumor recurrence after curative resection. This study aimed to analyze the factors related to recurrence after curative rectal cancer surgery, using variable machine learning techniques.

Correction to: Machine learning techniques for pathogenicity prediction of non-synonymous single nucleotide polymorphisms in human body

Correction to: Machine learning techniques for pathogenicity prediction of non-synonymous single nucleotide polymorphisms in human body

Intelligent framework for automated failure prediction, detection, and classification of mission critical autonomous flights

Autonomous flights are the major industry contributors towards next-generation developments in pervasive and ubiquitous computing. Modern aerial vehicles are designed to receive actuator commands from the primary autopilot software as input to regulate their servos for adjusting control surfaces. Due to real-time interaction with the actual physical environment, there exists a high risk of control surface failures for engine, rudder, elevators, and ailerons etc. If not anticipated and then timely controlled, failures occurring during the flight can have severe and cataclysmic consequences, which may result in mid-air collision or ultimate crash. Humongous amount of sensory data being generated throughout mission-critical flights, makes it an ideal candidate for applying advanced data-driven machine learning techniques to identify intelligent insights related to failures for instant recovery from emergencies. In this paper, we present a novel framework based on machine learning techniques for failure prediction, detection, and classification for autonomous aerial vehicles. The proposed framework utilizes long short-term memory recurrent neural network architecture to analyze time series data and has been applied at the AirLab Failure and Anomaly flight dataset, which is a comprehensive publicly available dataset of various fault types in fixed-wing autonomous aerial vehicles’ control surfaces. The proposed framework is able to predict failure with an average accuracy of 93% and the average time-to-predict a failure is 19 s before the actual occurrence of the failure, which is 10 s better than current state-of-the-art. Failure detection accuracy is 100% and average detection time is 0.74 s after happening of failure, which is 1.28 s better than current state-of-the-art. Failure classification accuracy of proposed framework is 100%. The performance analysis shows the strength of the proposed methodology to be used as a real-time failure prediction and a pseudo-real-time failure detection along with a failure classification framework for eventual deployment with actual mission-critical autonomous flights.

Machine learning techniques for pathogenicity prediction of non-synonymous single nucleotide polymorphisms in human body

Machine learning techniques for pathogenicity prediction of non-synonymous single nucleotide polymorphisms in human body

A Screening System for COVID-19 Severity using Machine Learning

COVID-19 disease can be classified into various stages depending on the severity of the patient. Patients in severe stages of COVID-19 need immediate treatment and should be placed in a medical-ready environment because they are at high risk of death. Thus, hospitals need a fast and efficient method to screen large numbers of patients. The enormous amount of medical data in public repositories allows researchers to gain information and predict possible outcomes. In this study, we use a publicly available dataset from Springer Nature repository to discuss the performance of three machine learning techniques for prediction of severity of COVID-19: Random Forest (RF), Naïve Bayes (NB) and Gradient Boosting (GB). These techniques were selected for their good performance in medical predictive analytics. We measured the performance of the machine learning techniques using six measurements (accuracy, precision, recall, F1-score, sensitivity and specificity) in predicting COVID-19 severity. We found that RF generates the highest performance score, which is 78.4, compared with NB and GB. We also conducted experiments with RF to establish the critical symptoms in predicting COVID-19 severity, and the findings suggested that seven symptoms are substantial. Overall, the performance of various machine learning techniques to predict severity of COVID-19 using electronic health records indicates that machine learning can be successfully applied to determine specific treatment and effective triage.

RECENT TRENDS OF MACHINE LEARNING PREDICTIONS USING OPEN DATA: A SYSTEMATIC REVIEW

Machine learning (ML) prediction determinants based on open data (OD) are investigated in this work, which is accomplished by examining current research trends over ten years. Currently, OD is commonly regarded as the most crucial trend for users to improve their ability to make decisions, particularly to the exponential expansion of social networking sites (SNSs) and open government data (OGD).The purpose of this study was to examine if there was an increase in the usage of OD in ML prediction techniques by conducting a systematic literature review (SLR) of the results of the trends. The papers published in major online scientific databases between 2011 and 2020, including ScienceDirect, Scopus, IEEE Xplore, ACM, and Springer, were identified and analysed. After various selection and Springer, were identified and analysed. After various selection processes, according to SLR based on precise inclusion and exclusion criteria, a total of 302 articles were located. However, only 81 of them were included. The findings were presented and plotted based on the research questions (RQs). In conclusion, this research could be beneficial to organisations, practitioners, and researchers by providing information on current trends in the implementation of ML prediction using OD setting by mapping studies based on the RQs designed, the most recent growth, and the necessity for future research based on the findings.

Analysis of Tree-Family Machine Learning Techniques for Risk Prediction in Software Requirements

Risk prediction is the most sensitive and critical activity in the Software Development Life Cycle (SDLC). It might determine whether the project succeeds or fails. To increase the success probability of a software project, the risk should be predicted at the early stages. This study proposed a novel model based on the requirement risk dataset to predict software requirement risks using Tree-Family-Machine-Learning (TF-ML) approaches. Moreover, the proposed model is compared with the state-of-the-art models to determine the best-suited methodology based on the nature of the dataset. These strategies are assessed and evaluated using a variety of metrics. The findings of this study may be reused as a baseline for future studies and research, allowing the results of any proposed approach, model, or framework to be benchmarked and easily checked.

Artificial intelligence & crime prediction: A systematic literature review

The security of a community is its topmost priority; hence, governments must take proper actions to reduce the crime rate. Consequently, the application of artificial intelligence (AI) in crime prediction is a significant and well-researched area. This study investigates AI strategies in crime prediction. We conduct a systematic literature review (SLR). Our review evaluates the models from numerous points of view, including the crime analysis type, crimes studied, prediction technique, performance metrics and evaluations, strengths and weaknesses of the proposed method, and limitations and future directions. We review 120 research papers published between 2008 and 2021 that cover AI approaches for crime prediction. We provide 34 crime categories researched by researchers and 23 distinct crime analysis methodologies after analyzing the selected research articles. On the other hand, we identify 64 different machine learning (ML) techniques for crime prediction. In addition, we observe that the most applied approach in crime prediction is the supervised learning approach. Furthermore, we discuss the evaluation and performance metrics, as well as the tools utilized in building the models and their strengths and weaknesses. Crime prediction AI techniques are a promising field of study, and there are several ML models that researchers have applied. Consequently, based upon this review, we provide advice and guidance for researchers working in this area of study.

EARLY PREDICTION OF CERVICAL CANCER USING MACHINE LEARNING TECHNIQUES

According to recent studies and statistics, Cervical Cancer (CC) is one of the most common causes of death worldwide, and mainly in the developing countries. CC has a mortality rate around 60%, in less developing countries and the percentages could go even higher, due to poor screening processes, lack of sensitization, and several other reasons. Therefore, this paper aims to utilize the high capabilities of machine learning techniques in the early prediction of CC. In specific, three well-known feature selection and ranking methods have been used to identify the most significant features that help in the diagnosis process. Also, eighteen different classifiers that belong to six learning strategies have been trained and extensively evaluated against a primary data which consists of five hundred images. Moreover, an investigation regarding the problem of imbalance class distribution which is common in medical dataset is conducted. The results revealed that LWNB and RandomForest classifiers showed the best performance in general, and considering four different evaluation metrics. Also, LWNB and Logistic classifiers were the best choices to handle the problem of imbalance class distribution which is common in medical diagnosis task. The final conclusion could be made is that using an ensemble model which consists of several classifiers such as LWNB, RandomForest, and Logistic is the best solution to handle this type of problems.

Application of IoT and Machine Learning Techniques for Heart Disease Prediction and Diagnosis: A Comprehensive Review

Heart diseases and related disorders have emerged as the most dangerous and leading cause of global deaths affecting mostly the elderly people who suffer from these diseases without realizing or knowing about it. Due to the fact that it’s very hard to notice the signs of the sickness at an early stage, mostly the signs will be shown once the heart problem has reached the peak level. This paper therefore presents a detailed review of how to monitor and predict heart disease using machine learning and the IoT. With the help of IoT, patients and doctors will monitor cardiovascular diseases early enough. A comparative analysis of different IoT technologies, most of which employed machine learning approaches for predicting and diagnosing cardiovascular disease, is conducted, different methodologies are compared and the results analysis is conducted and the performance tabulated. The Internet of Things (IoT) is transforming embedded systems into networked smart gadgets with sensors. The primary drawback of employing smart devices was their limited storage and processing capability, which cloud computing addressed by providing high-level processing and storage capacities. The Internet of Things (IoT) is transforming embedded systems into networked smart gadgets with sensors. The most significant disadvantage of using smart devices was their low cost. The problem of limited storage capacity and processing power was solved by cloud computing.

Applying machine learning techniques in air quality prediction

Air pollution levels become more and more dangerous every year, which is one оf the most significant and important problems for humanity nowadays. Of all air pollutants, particulate matter has the most harmful effect on the human health, especially in the long term. This article discusses the need to predict changes in PM2.5 concentrations for air quality monitoring and preventing unsafe and risky situations. In the course of the presented study, detailed visualization of the original data and а comparative survey of the application of machine learning methods were carried out. For solving the problem of forecasting time series of particulate matter concentration values several machine learning models, such as ARIMA, Facebook Prophet, and LSTM, were used. At the moment, it is possible to predict the trend of changes in the mean values of pollutant concentrations for several months in advance. But widespread use of these technologies requires more correct data to make better and more accurate predictions.

House Price Prediction using Random Forest Machine Learning Technique

Predicting a price variance rather than a specific value is more realistic and attractive in many real-world applications. Price prediction can be thought of as a classification issue in this situation. However, the House Price Index (HPI) is a common tool for estimating the inconsistencies of house prices. Since housing prices are closely correlated with other factors such as location, city, and population, predicting individual housing prices needs information other than HPI. The HPI is a repeat-sale index that tracks average price shifts in repeat transactions or refinancing of the same assets. Therefore, HPI is ineffective at predicting the price of a single house because it is a rough predictor based on all transactions. This study explores the use of Random Forest machine learning technique for house price prediction. UCI Machine learning repository Boston housing dataset with 506 entries and 14 features were used to evaluate the performance of the proposed prediction model. A comparison of the predicted and actual prices predicted revealed that the model had an acceptable predicted value when compared to the actual values with an error margin of ±5.

Well-Logging Prediction Based on Hybrid Neural Network Model

Well-logging is an important formation characterization and resource evaluation method in oil and gas exploration and development. However, there has been a shortage of well-logging data because Well-logging can only be measured by expensive and time-consuming field tests. In this study, we aimed to find effective machine learning techniques for well-logging data prediction, considering the temporal and spatial characteristics of well-logging data. To achieve this goal, the convolutional neural network (CNN) and the long short-term memory (LSTM) neural networks were combined to extract the spatial and temporal features of well-logging data, and the particle swarm optimization (PSO) algorithm was used to determine hyperparameters of the optimal CNN-LSTM architecture to predict logging curves in this study. We applied the proposed CNN-LSTM-PSO model, along with support vector regression, gradient-boosting regression, CNN-PSO, and LSTM-PSO models, to forecast photoelectric effect (PE) logs from other logs of the target well, and from logs of adjacent wells. Among the applied algorithms, the proposed CNN-LSTM-PSO model generated the best prediction of PE logs because it fully considers the spatio-temporal information of other well-logging curves. The prediction accuracy of the PE log using logs of the adjacent wells was not as good as that using the other well-logging data of the target well itself, due to geological uncertainties between the target well and adjacent wells. The results also show that the prediction accuracy of the models can be significantly improved with the PSO algorithm. The proposed CNN-LSTM-PSO model was found to enable reliable and efficient Well-logging prediction for existing and new drilled wells; further, as the reservoir complexity increases, the proxy model should be able to reduce the optimization time dramatically.

Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults.

As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the “Healthy Ageing Initiative” study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.

Features fusion based link prediction in dynamic neworks

Link prediction is the process of detecting new or missing links in any network. This work focuses on link prediction in dynamic networks. One of the major challenges of the link prediction problem is to achieve high accuracy. One of the well-known methods for link prediction is the similarity-based method, which uses similarity-based score. The three widely used similarity-based indices are Local (L), Global (G), and Quasi-local (Q) for calculating similarity scores. In this work, we have proposed a model, namely the LGQ model. In our LGQ model, the wide categories of indices are used in different combinations(L, G, Q, LG, LQ, GQ, LGQ) for feature set generation that can be used with various machine learning techniques for link prediction. In local similarity indices, we have considered Common Neighbors (CN), Adamic/Adar Index (AA), Jaccard Coefficient (JC), and Preferential Attachment (PA). In global similarity indices, we have considered cos+, Average Commute Time (ACT), Shortest Path (SP), MFI (Matrix Forest Index), and in quasi-local indices, local path Index (LP), Path of Length 3 (L3). We have tested and validated our proposed model by conducting numerical simulations on six well-known dynamic network datasets. The results demonstrate that the proposed LGQ model and its variations outperform several baseline algorithms in terms of AUPR, F1 score, Balanced accuracy score (BAC), and AUC. We also demonstrate and contrast the preference of Neural Network and Xgboost based prediction models for different variations of proposed feature sets.

A Comprehensive Review of Various Machine Learning Techniques for Heart Disease Prediction

A Comprehensive Review of Various Machine Learning Techniques for Heart Disease Prediction

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.

Machine learning techniques for mortality prediction in emergency departments: a systematic review

ObjectivesThis systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments...