Smart HealthCare Model: An End-to-end Framework for Disease Prediction and Recommendation of Drugs and Hospitals

2020 ◽  
pp. 245-264
Author(s):  
Megha Rathi ◽  
Nimit Jain ◽  
Priya Bist ◽  
Tarushi Agrawal
Keyword(s):  
Disease Prediction ◽  
Smart Healthcare ◽  
Healthcare Model ◽  
End To End

scholarly journals An IoMT-Enabled Smart Healthcare Model to Monitor Elderly People Using Machine Learning Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Muhammad Farrukh Khan ◽  
Taher M. Ghazal ◽  
Raed A. Said ◽  
Areej Fatima ◽  
Sagheer Abbas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elderly People ◽  
Nearest Neighbor ◽  
Quality Of Healthcare ◽  
Healthcare Services ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Smart Healthcare ◽  
Healthcare Model ◽  
The Cost

The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniques, e.g., support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT), to analyze the effectiveness of the result.

Disease Prediction using Naïve Bayes - Machine Learning Algorithm

2021 ◽  
Vol 6 (4) ◽  
pp. 17-22
Author(s):  
Chandrasekhar Rao Jetti ◽  
Rehamatulla Shaik ◽  
Sadhik Shaik
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Healthcare System ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Disease Prediction ◽  
Machine Learning Algorithm ◽  
Data Mining Techniques ◽  
Smart Healthcare

It can occur on many occasions that you or a loved one requires urgent medical assistance, but they are unavailable due to unforeseen circumstances, or that we are unable to locate the appropriate doctor for the care. As a result, we will try to incorporate an online intelligent Smart Healthcare System in this project to solve this issue. It's a web-based programmed that allows patients to get immediate advice about their health problems. The aim of the smart healthcare system is to create a web application that can take a user's symptoms and predict diseases, as well as serve as an online consultant for various diseases. We created an expert system called Smart Health Care System, which is used to make doctors' jobs easier. A machine examines a patient at a basic level and recommends diseases that may be present. It begins by inquiring about the patient's symptoms; if the device is able to determine the relevant condition, it then recommends a doctor in the patient's immediate vicinity. The system will show the result based on the available accumulated data. We're going to use some clever data mining techniques here. We use several intelligent data mining techniques to guess the most accurate illness that could be associated with a patient's symptoms, and we use an algorithm (Naive Bayes) to map the symptoms with potential diseases based on a database of many patients' medical records. This system not only makes doctors' jobs easier, but it also benefits patients by getting them the care they need as soon as possible. Keywords: Disease Prediction, Naïve Bayes, Machine Learning Algorithm, Smart Healthcare System.

Smart healthcare model with fog-cloud network architecture

2019 ◽  
Vol 1 (2/3) ◽  
pp. 99
Author(s):  
Mrinal Kanti Naskar ◽  
Amitava Mukherjee ◽  
Rohan Basu Roy ◽  
Arani Roy
Keyword(s):  
Network Architecture ◽  
Smart Healthcare ◽  
Healthcare Model ◽  
Cloud Network

scholarly journals Automatic Heart Disease Classification Using Ensemble Features Extraction Mechanism from ECG Signals

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 790-805
Author(s):  
Avinash L. Golande ◽  
T. Pavankumar
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Cost Effective ◽  
Disease Classification ◽  
Disease Prediction ◽  
Prediction System ◽  
Healthcare Applications ◽  
Hybrid Features ◽  
Smart Healthcare ◽  
Hybrid Mechanism

The heart disease detection and classification using the cost-effective tool electrocardiogram (ECG) becomes interesting research considering smart healthcare applications. Automation, accuracy, and robustness are vital demands for an ECG-based heart disease prediction system. Deep learning brings automation to the applications like Computer-Aided Diagnosis (CAD) systems with accuracy improvement compromising robustness. We propose the novel ECG-based heart disease prediction system using the hybrid mechanism to satisfy the automation, accuracy, and robustness requirements. We design the model via the steps of pre-processing, hybrid features formation, and classification. The ECG pre-processing is aiming at suppressing the baseline and powerline interference without loss of heartbeats. We propose a hybrid mechanism that consists of handcrafted and automatic Convolutional Neural Network (CNN) lightweight features for efficient classification. The hybrid feature vector is fed to the deep learning classifier Long Term Short Memory (LSTM) sequentially to predict the disease. The simulation results show that the proposed model reduces the diagnosis errors and time compare to state-of-art methods.

scholarly journals Multitask Healthcare Management Recommendation System Leveraging Knowledge Graph

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wanheng Liu ◽  
Ling Yin ◽  
Cong Wang ◽  
Fulin Liu ◽  
Zhiyu Ni
Keyword(s):  
Deep Neural Network ◽  
Recommendation System ◽  
State Of The Art ◽  
Healthcare Management ◽  
Treatment Programs ◽  
Treatment Recommendation ◽  
Knowledge Graph ◽  
Disease Prediction ◽  
Smart Healthcare ◽  
5G Network

In this paper, a novel multitask healthcare management recommendation system leveraging the knowledge graph is proposed, which is based on deep neural network and 5G network, and it can be applied in mobile and terminal device to free up medical resources and provide treatment programs. The technique we applied is referred to as KG-based recommendation system. When several experiments have been carried out, it is demonstrated that it is more intelligent and precise in disease prediction and treatment recommendation, similar to the state of the art. Also, it works well in the accuracy and comprehension, which is much higher and highly consistent with the predictions of the theoretical model. The fact that our work involves studies of multitask healthcare management recommendation system, which can contribute to the smart healthcare development, proves to be promising and encouraging.

Proposing Real-Time Smart Healthcare Model Using IoT

2020 ◽  
pp. 25-41
Author(s):  
Rashbir Singh ◽  
Prateek Singh ◽  
Latika Kharb
Keyword(s):  
Real Time ◽  
Smart Healthcare ◽  
Healthcare Model

A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion

2020 ◽  
Vol 63 ◽  
pp. 208-222 ◽  
Author(s):  
Farman Ali ◽  
Shaker El-Sappagh ◽  
S.M. Riazul Islam ◽  
Daehan Kwak ◽  
Amjad Ali ◽  
...  
Keyword(s):  
Heart Disease ◽  
Deep Learning ◽  
Monitoring System ◽  
Feature Fusion ◽  
Disease Prediction ◽  
Smart Healthcare ◽  
Healthcare Monitoring

scholarly journals Machine Learning Driven IoT Based Smart Health Care Kit

2021 ◽  
Author(s):  
Lekhasree Narayanagari ◽  
Baidya Nath Saha
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Lung Disease ◽  
Health Indicators ◽  
Disease Prediction ◽  
Time Data ◽  
2Nd Generation ◽  
Smart Healthcare ◽  
Deep Architecture ◽  
Intel Galileo

This paper focuses on developing a machine learning driven IOT based smart healthcare kit. It plays an important role in emergency medical service like Intensive Care Units (ICU), by using an INTEL GALILEO 2ND generation development board. It facilitates to monitor and track different health indicators such as Blood Pressure, Pulses, and Temperature of the patient. This system allows to send the real time data of a patient to the physician and record it for future use. In this research we conducted two experiments: a)heart disease prediction from pathology data and b) lung disease prediction from X-ray images. For heart disease prediction we evaluate the performance of K-Nearest Neighbour and Random Forest Classifier and for lung disease prediction, we use VGG19 deep architecture. Experimental results demonstrate that machine learning can help to automate the IoT based smart healthcare kit and help doctors to diagnose the diseases.

Smart healthcare model with fog-cloud network architecture

2019 ◽  
Vol 1 (2/3) ◽  
pp. 99
Author(s):  
Rohan Basu Roy ◽  
Arani Roy ◽  
Amitava Mukherjee ◽  
Mrinal Kanti Naskar
Keyword(s):  
Network Architecture ◽  
Smart Healthcare ◽  
Healthcare Model ◽  
Cloud Network
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