Health Care Analysis Using Machine Learning Thesis topics
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- Mental Healthcare Analysis using Power BI & Machine Learning
Keywords:
Mental Health, Data Analysis, Power BI, Machine Learning
The aim of our paper is to recognize the factor and identify those factors that are responsible for person’s poor mental health. At first we analyse and point out the cause for poor mental health and then we have to gather data of various type of person from various professions. After gathered data, we have to preprocess the data and then we use ML methods for classification and prediction.
- A Comparative Analysis of Fraud Detection in Healthcare using Data Balancing & Machine Learning Techniques
Keywords:
Exploratory Data Analysis (EDA), Class Weighing Scheme (CWS), Adaptive Synthetic Oversampling (ADASYN)
Our work utilizes a large dataset so that we have to perform Exploratory Data Analysis (EDA). Then we preprocess the data and feature engineering to generate a possible dataset for further analysis. Our proposed method shows a comparative analysis of outcome of various ML methods by utilizing two balancing methods that is CWS and ADASYN for oversampling of outcome with unbalanced dataset.
- Behaviour Analysis Using Machine Learning Algorithms In Health Care Sector
Keywords:
Behavioral analytics, Algorithm, accuracy, healthcare services
Our paper examines some ML methods utilized in early disease detection and finds the key trends in performance. We utilize some ML methods combined with healthcare services are NB, SVM, RF and CNN. We have a variety of cancer classification in that our models have proved to be increased efficiency in analysing different cancer types. We have to generate computational model that permit disease prediction and management to become accurate.
- Data Mining in Healthcare using Machine Learning Techniques
Keywords:
Heart disease, Classification algorithm, Data Mining, Artificial Intelligence, Python
We explore the utilization of classification methods in data mining for healthcare applications. Our aim is to apply classification methods like NB, LR and RF to healthcare datasets and to estimate their performance. We used the dataset that may contain patient data such as medical history, demographics and diagnosis. Our outcome shows that the classification method can be effective in healthcare application.
- Machine learning model for healthcare investments predicting the length of stay in a hospital & mortality rate
Keywords:
Health services research, Neural network, Public health, Health economy, Patient monitoring, Length of stay analysis, Medical data transformation, Clinical intelligence, Survival analysis, Mortality, Bed management, Prediction monitoring
Our paper aims to improve the ML model to predict long-term outcomes like Length of Stay (LOS), mortality rate of a patient admitted into the hospital. We utilized National Hospital Care Research Database (NHCRD) to generate a low feature based predictive model with suitable performance. We evaluate some metrics by utilizing various ML methods like RF, LR, GB, DT, NB, ANN and EL methods.
- Application of Healthcare Management Technologies for COVID-19 Pandemic Using Internet of Things and Machine Learning Algorithms
Keywords:
IoT, COVID-19, Healthcare system, Medical, Corona virus
Our paper summaries the job of IoT based improvements in COVID19 and reviews the best-in-class structure, stages, applications and modern IoT based arrangement struggling COVID19 of each three primary stages, namely early conclusion, quarantine time, and after recuperation. Finally, our study utilized the prediction of healthcare with improved accuracy and then SVM and KNN are the best methods. Then NB, DT, Decision stump has followed it.
- Healthcare framework for identification of strokes based on versatile distributed computing and machine learning
Keywords:
Deep neural networks, Strokes, Distributed computing, Healthcare
We offer a novel healthcare framework that influences versatile distributed computing and mobile computing to improve the delivery. Our suggested frame contains two fundamental components namely flexible usage and server request. We used CNN to diffentiate two-stroke subtypes and GBRF for analysis and accurately predict healthcare system. At first, we improve the healthcare service, next verify the gathered data and at last inform the health status of patient among CNN API. By combining machine intelligence-based methods we enhance the efficiency and effectiveness of stroke finding.
- Application of Machine Learning in Healthcare: An Analysis
Keywords:
Deep Learning, Personalized healthcare Introduction
Our paper analyse ML is the best method to increase health care services. ML has been effectively used for disease prediction, disease detection, providing personalized healthcare etc.. We used both supervised and unsupervised method in this field. We have to gather the data from wearable devices and sensors to be processed by utilizing ML and that can lead to quality-of-life enhancement.
- Machine Learning Platform for Remote Analysis of Primary Health Care Technology to Support Ubiquitous Management in Clinical Engineering
Keywords:
Clinical engineering, Ubiquitous health technology management
Our paper offers the process of utilizing remote analysis of healthcare technology conditions through ML to support the decision-making process of all-over management in clinical engineering. Our method can be applied to dental technology and it was developed in Microsoft azure ML studio platform that test the methods like NN, LR, Decision Jungle and Decision Forest, after comparison we will get the best method.
- Automatic detection of vocal cord disorders using machine learning method for healthcare system
Keywords:
Sequential Learning, Resource Allocation Neural Network, Wavelet Transform
Our work presents a Sequential Learning Resource Allocation Neural Network (SL-RAN) to overcome the existing drawback. At first, we can preprocess the voice signals by utilizing discrete wavelet transform method and the voice disorder detection can be extracted by utilizing a Mel Frequency Cepstrum feature extraction method. After we remove the features SL-RAN classifies the type of vocal cord disorder.