Artificial Intelligence based prediction system for General Medicine

COVID-19 is highly infectious and has been extensively spread worldwide, with approximately 651 million definite cases crosswise the globe including Pakistan. At that era of pandemic where patients are not able to approach a doctor for even the routine checkups, in such curial situation even normal disease checkups are ignored by many families due to pandemic situations, those diseases may lead to be a perilous disease are results of it. Human disorders portray scenarios that even disturb or permanently cutoff the essential functions of a body parts. Consequently, the aim is to transform raw health data potential into actionable insights to applying the promising outcomes of Body Sensor Network (BSN) and State-of-Art Artificial Intelligence (AI) techniques to get proper medicine allocation to the particular health state of patient. In this paper the different techniques of Deep Learning and Machine Learning introduced to predict the actual medicine for the specific health state of patient according to data from the BSN. Experiments have been conducted on large dataset which shepherd it into 16 states of patient's health which will allotted to AI model to predict the medicine accordingly to the health state of patient. Experimental results show the 87.46% by Random Forest, 92.74% by K-Nearest Neighbors, 74.57% by Naive Bayes, 94.41% by Extreme Gradient Boost, 84.88% by Multi-Layer Perceptron in terms of precision of model training in event of classification. © 2023 IEEE.

A Decision Support System using Machine Learning -IoT to tackle the Covid-19 pandemic in Uttarakhand

Covid-19 spread is worldwide;India is now at the second place where this epidemic is spreading with high rate. The state of Uttarakhand, a hilly state of India also has a significant impact of Covid-19. This paper suggests that machine learning techniques with IOT can equipped the doctors, and lab technicians to deal with this pandemic. Here, we also design a prediction system to help the doctors so that they can keep the records of infected patients. We used IoT, machine learning and ensemble methods for healthcare to store infected patient's data in the cloud database, and enable doctors/others to screen patient's data about their disease. We developed a decision support system to detect the diseases quickly and the treatment can be initiated immediately. © 2022 IEEE.

Career Path Prediction System Using Supervised Learning Based on Users' Profile

In the past few years, technology has changed drastically and due to COVID-19 pandemic, people spend more time on screen. The use of social media platforms has also been increased and this affects the human mind and decision taking ability. Online career counseling is largely supported these days and hence this paper proposes an online career prediction system using supervised machine learning based on the user's profile. This research attempted to develop a model for the user which predicts the career path in a precise manner and gives actionable feedback and career recommendations to encourage them to make significant career judgments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Estimating the Quality of Information Technology Services Using the Generalized Additive Regression

When covid-19 attacked the world, all activities in the economic, social, cultural, and educational fields which were initially carried out onsite, were all done online, especially education at Telkom University which mostly uses website-based applications such as LMS, Igracias, SiRAMA as learning media center. So, the possibility of problems with the website is very large. To overcome this, students can issue a ticketing to ask for assistance from IT support at Telkom University, namely the Information Technology Services. Therefore, the role of the Information Technology Services in online education is very important, because the service is directly dealing with problems that occur in every student of Telkom University. In this research, the author proposes a prediction system for the quality of service of the Information Technology Services to predict whether the services of the Information Technology Services have been maximized and have greatly helped Telkom University students. The author uses the Generalized Additive Model for regression algorithm to estimate the quality of the Information Technology Servicesector's service which is selected due to its flexibility. © 2022 IEEE.

Pressure Prediction System in Lung Circuit Using Deep Learning

A massive number of patients infected with SARS-CoV2 and Delta variant of COVID-19 have generated acute respiratory distress syndrome (ARDS) which needs intensive care, which includes mechanical ventilation. But due to the huge no of patients, the workload and stress on healthcare infrastructure and related personnel have grown exponentially. This has resulted in huge demand for innovation in the field of automated health care which can help reduce the stress on the current healthcare infrastructure. This work gives a solution for the issue of pressure prediction in mechanical ventilation. The algorithm suggested by the researchers tries to predict the pressure in the respiratory circuit for various lung conditions. Prediction of pressure in the lungs is a type of sequence prediction problem. Long short-term memory (LSTM) is the most efficient solution to the sequence prediction problem. Due to its ability to selectively remember patterns over the long term, LSTM has an edge over normal RNN. RNN is good for short-term patterns but for sequence prediction problems, LSTM is preferred. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data

The recent dramatic expansion of the COVID-19 outbreak is placing enormous strain on human society as a whole. Numerous biomarkers are being investigated in an effort to track the condition of the patient. This could interfere with signs of many other illnesses, making it more difficult for a specialist to diagnose or predict the severity level of the case. As a result, the focus of this research was on the development of a multiclass prediction system capable of dealing with three severity cases (severe, moderate, and mild). The lymphocyte to CRP ratio (C-reactive protein blood test) and SpO2 (blood oxygen saturation level) indicators were ranked and used as prediction system attributes. A machine learning model based on SVMs is created. A total of 78 COVID-19 patients were recruited from the Azizia primary health care sector/Wasit Health Directorate/Ministry of Health to form different combinations of COVID-19 clinical dataset. The outcomes demonstrate that the proposed approach had an average accuracy of 82%. The established prediction system allows for the early identification of three severity cases, which reduces deaths. © 2022 Ahmed M. Dinar et al.

A Mexico's Covid Traffic Light Color Prediction System Based on Mexican News

The COVID-19 pandemic has brought social life to a near standstill as many countries imposed very strict restrictions on movement to halt the spread of the virus. In Mexico, a traffic light system was implemented to indicate the crisis level to inform the society of the restrictions for each of the color stages of the system. The present work is an attempt to predict the traffic light color at the current week, and also perform a prediction for 2, 4, and even 8 weeks ahead by using Mexican news. For this work, we consider two approaches, one based on features extracted directly from the news and the other applying transfer learning. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Health Insurance Cost Prediction using Machine Learning

This paper represents a machine learning-based health insurance prediction system. Recently, many attempts have been made to solve this problem, as after Covid-19 pandemic, health insurance has become one of the most prominent areas of research. We have used the USA's medical cost personal dataset from kaggle, having 1338 entries. Features in the dataset that are used for the prediction of insurance cost include: Age, Gender, BMI, Smoking Habit, number of children etc. We used linear regression and also determined the relation between price and these features. We trained the system using a 70-30 split and achieved an accuracy of 81.3%. © 2022 IEEE.

An UAV Assisted Need Assessment and Prediction System for COVID-19 Vaccination

Health monitoring by government in rural and Urban areas become very much challenging task as they require huge amount of technicians, doctors and funds to complete. In the time of COVID-19 pandemic, it is difficult to allow doctors to visit rural areas for monitoring the health of public, rather than allocate their duties in COVID-19 hospitals to save critical patients. But, it is also necessary to monitor health of public to vaccinate them priority wise in the scarcity of COVID-19 vaccines. In this paper we have proposed a novel UAV (Unmanned Aerial Vehicle) assisted health monitoring system which can be operated in any remote location to get required data about the health condition of the people. After collecting the desired data from the user, system saves them in memory. In the control room, UAV uploads the collected data to the server for analysis. From the analysed data the system can decide whom need to be vaccinated immediately. UAV system will analyse the data with respect to different parameters like age, co-morbidity, blood pressure and other attributes. From this analysed data using machine learning algorithm, system also predicts how many days might be taken to complete the whole vaccination process. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Aspect-Level Drug Reviews Sentiment Analysis and COVID-19 Drug prediction using PPI & Deep Learning

The immense pressure and tension has created in the worldwide healthcare systems by disease. Various existing system has defined drug prediction system based on current patient evaluation. In this research we proposed a drug prediction for COVID-19 patient based on protein to protein reactions and availability. In order to evaluate the protein-protein interactions (PPIs) between some of the virus and individual receptors that are also confirmed utilizing biomedical simulations, the framework also defines machine learning models. The classification techniques are consistent with the predictions of separate physical material sequence-based characteristics such as classification of amino acids, distribution of pseudo amino acids and conjoint triads. Finally we will evaluate the system with numerous machine learning algorithm and show the effectiveness of propose systems. © 2021 IEEE.

Smart Stethoscope: Intelligent Respiratory Disease Prediction System

Diagnosing and treating lung diseases can be challenging since the signs and symptoms of a wide range of medical conditions can indicate interstitial lung diseases. Respiratory diseases impose an immense worldwide health burden. It is even more deadly when considering COVID-19 in present times. Auscultation is the most common and primary method of respiratory disease diagnosis. It is known to be non-expensive, non-invasive, safe, and takes less time for diagnosis. However, diagnosis accuracy using auscultation is subjective to the experience and knowledge of the physician, and it requires extensive training. This study proposes a solution developed for respiratory disease diagnosis. 'smart Stethoscope' is an intelligent platform for providing assistance in respiratory disease diagnosis and training of novice physicians, which is powered by state-of-the-art artificial intelligence. This system performs 3 main functions(modes). These 3 modes are a unique aspect of this study. The real-time prediction mode provides real-time respiratory diagnosis predictions for lung sounds collected via auscultation. The offline training mode is for trainee doctors and medical students. Finally, the expert mode is used to continuously improve the system's prediction performance by getting validations and evaluations from pulmonologists. The smart stethoscope's respiratory disease diagnosis prediction model is developed by combining a state-of-the-art neural network with an ensembling convolutional recurrent neural network. The proposed convolutional Bi-directional Long Short-Term Memory (C- Bi LSTM) model achieved an accuracy of 98% on 6 class classification of breathing cycles for ICBHF17 scientific challenge respiratory sound database. The novelty of the project lies on the whole platform which provides different functionalities for a diverse hierarchy of medical professionals which supported by a state-of-the-art prediction model based on Deep Learning. © 2022 IEEE.

IoT-enabled model for Digital Twin of Mental Stress (DTMS)

Stress has become one of the mental health adversaries of the COVID-19 pandemic. Several stressors like fear of infection, lockdown, and social distancing are commonly accountable for the stress. The existing stress prediction systems are less compatible to handle diversly changing stressors during COVID-19. The traditional approaches often use incomplete features from limited sources (e.g., only wearable sensor or user device) and static prediction techniques. The Edge Artificial Intelligence (Edge AI) employs machine learning to make data from these sources usable for decision making. Therefore, In this study, we propose a Digital Twin of Mental Stress (DTMS) model that employs IoT-based multimodal sensing and machine learning for mental stress prediction. We obtained 98% accuracy for four widely used Machine Learning(ML) algorithms Naïve Bayes(NB), Random Forest(RF), Multilayer Perceptron(MLP), and Decision Tree (DT). The optimal Digital Twin Features (DTF) could reduce the classification time. © 2021 IEEE.

COVID-19 Future Forecasting Tool: Infected Patients Recovery and Hospitalization Trends Using Deep Learning Models

This paper presents a deep learning-based prediction system tool for COVID-19 patients using ARIMA, LSTM, and prophet hybrid algorithms. COVID-19 pandemic poses a significant impact all over the world. However, when we get infected, we do not understand where we should go, whether we need to be hospitalized, which doctor we should consult, and how much it would cost. This work explores how to solve those problems using API technology and deep learning to help moderate those who endure COVID-19. Using this tool, the user will find a better hospital for their patient, and the tool will predict the hospital based on patient budget, location, recovery time. Overall, by analyzing the patient's data like age, gender, oxygen saturation level, tools will give suggestions. We collected 250 data as a testing data scenario and acquired the training data set from John Hopkins University, which is available on public platforms. So this proposed work has cooperated with the API and deep learning model;this tool contains ARIMA, LSTM, and Prophet hybrid model. API will update the new cases of coronavirus, recovery rate, and death rate. Finally, the proposed tool will predict a better solution base on those API data. © 2021 IEEE.