Machine Learning Techniques in Healthcare—A Survey

2020 ◽  
Vol 17 (9) ◽  
pp. 4276-4279
Author(s):  
K. Aiswariya Milan ◽  
Niharika P. Kumar
Keyword(s):  
Machine Learning ◽  
Medical Diagnosis ◽  
Urban Areas ◽  
Treatment Plan ◽  
Disease Outbreaks ◽  
Data Access ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Personalized Healthcare

The development of science and technology has led to a very busy lifestyle among urban people across the globe. Due to the advent of cutting-edge technologies, connectivity and networking is a boon to the people living in urban areas. Thus, a vast amount of patient data from admission, treatment and discharge is collected across the clinical community. These rich data being available online has been under-utilized and the question arises on how best the data can be utilized. With the centralized data and powerful data analytical algorithms are running in powerful machines, until recent past, the machine learning is yet to be used for improving the diagnosis, prediction and secure data access process in healthcare. In this proposal, machine learning algorithms are used for enhanced medical diagnosis, personalized healthcare, predicting disease outbreaks in certain regions and measures for securing healthcare data from malicious attacks. The work focuses on 3 major chronic diseases such as Heart Attack, Stroke and Diabetics. Enhanced medical diagnosis involves the methods for predicting readmissions to hospital after X days of their discharge. Personalized healthcare involves methods for disease diagnosis and building treatment plan. The predictions are based upon on the patient’s medical reports and living habits. Disease outbreaks in an area involves methods for monitoring and predicting epidemic outbreaks in an area, during certain period of time based on information from social media.

scholarly journals Survey on machine learning algorithms for liver disease diagnosis and prediction

2018 ◽  
Vol 7 (1.8) ◽  
pp. 99 ◽  
Author(s):  
M Kiran Kumar ◽  
M Sreedevi ◽  
Y C. A. Padmanabha Reddy
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Computer Aided Diagnosis ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

Machine learning plays a vital role in health care industry. It is very important in Computer Aided Diagnosis. Computer Aided Diagnosis is a quickly developing dynamic region of research in medicinal industry. The current specialists in machine learning guarantee the enhanced precision of discernment and analysis of diseases. The computers are empowered to think by creating knowledge by learning. This procedure enables the computers to self-learn individually without being explicitly programed by the programmer .There are numerous sorts of Machine Learning Techniques and which are utilized to classify the data sets. They are Supervised, Unsupervised and Semi-Supervised, Reinforcement, deep learning algorithms. The principle point of this paper is to give comparative analysis of supervised learning algorithms in medicinal area and few of the techniques utilized as a part of liver disease prediction.

scholarly journals Differential Diagnosis of Tuberculosis and Pneumonia using Machine Learning

2019 ◽  
Vol 8 (11) ◽  
pp. 245-250
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Differential Diagnosis ◽  
Medical Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Care Practice ◽  
Digital Revolution ◽  
Learning Techniques ◽  
Classification Prediction

Machine learning has become one of the top most emerging technologies in this era of digital revolution. The machine learning algorithms are being used in various fields and applications such as image recognition, speech recognition, classification, prediction, medical diagnosis etc. In medical domain, machine learning techniques have been successfully implemented to improve the accuracy of medical diagnosis and also to improve the efficiency and quality of health care. In this paper, we have analyzed the existing health care practice system and have proposed how machine learning techniques can be used for differential diagnosis of Tuberculosis and Pneumonia which are often misdiagnosed due to similar symptoms at early stages.

scholarly journals A Comparative study of machine learning algorithms on thyroid disease prediction

2018 ◽  
Vol 7 (2.8) ◽  
pp. 315 ◽  
Author(s):  
Shaik Razia ◽  
P SwathiPrathyusha ◽  
N Vamsi Krishna ◽  
N Sathya Sumana
Keyword(s):  
Machine Learning ◽  
Thyroid Hormones ◽  
Thyroid Disease ◽  
High Capacity ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Learning Techniques

Thyroid illness is a medicinal state that influences the functionality of the thyroid organ that is thyroid gland [1](Guyton, 2011).The indications of thyroid ailment differ basing upon the type. There are four most common varieties: hypothyroidism (low capacity) which is caused due to the insufficiency of the thyroid hormones; hyperthyroidism (high capacity) which is caused due to the existence of the thyroid hormones more than just sufficient, basic variations from the norm, most normally an augmentation of the thyroid organ; and tumors which can be benign or can cause cancer. It is additionally conceivable to have irregular thyroid capacity tests with no clinical side effects [2](Bauer & al, 2013).In this study a comparative thyroid disease diagnosis were performed by using Machine learning techniques that is Support Vector Machine (SVM), Multiple Linear Regression, Naïve Bayes, Decision Trees. For this purpose, thyroid disease dataset gathered from the UCI machine learning database was used.

A QUALITATIVE LITERATURE REVIEW OF MACHINE LEARNING TECHNIQUES USED FOR DIAGNOSIS OF NEONATAL DISEASES IN HEALTHCARE

2021 ◽  
pp. 4-7
Author(s):  
Madhura Ranade ◽  
Anupama Deshpande
Keyword(s):  
Machine Learning ◽  
Treatment Plan ◽  
Disease Diagnosis ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Detection ◽  
Neonatal Disease ◽  
Learning Techniques ◽  
Prediction And Prevention

Background:There has been signicant growth in the use of Articial Intelligence (AI) for healthcare in the last decade. Aim: To identify effective AI techniques for the prediction & diagnosis of neonatal diseases and preventive measures & treatment plan for them. Neonates are newborn babies less than a month old. Methods:Research papers published in databases like IEEE Xplore, Medline, PUBMED and Elsevier were searched to nd publications reporting the application of AI for the prediction and prevention of neonatal diseases. The overall search strategy was to retrieve articles that included terms that were related to “NICU”, “Articial Intelligence”, “Neonatal diseases” and “Healthcare”. Results: Hundreds of papers were identied in initial search, out of which 13 publications met the evaluation criteria of related terms inclusion, AI for Neonatal Diseases in particular. These papers described application of AI techniques in neonatal healthcare for disease detection and were summarized for nal analysis. Most of the papers are focused on using supervised machine learning techniques for the prediction of diseases. Various other approaches in AI techniques used in neonatal disease diagnosis have been tested for related ndings, factors, methods, to address and document performance metrics. The comparative analysis of ML model evaluation parameters like AUC (Area under Curve), Specicity, Sensitivity, True Positive and False-negative Rates was done to develop the scope for improving performance of AI/MLtechniques. Conclusion: The systematic study and review of different AI techniques such as supervised machine learning; articial neural networks, data mining techniques used for neonatal disease diagnosis highlighted their role in disease prediction, management, and treatment plan. More studies are needed to improve the use of AI for timely prediction of neonatal diseases like respiratory distress syndrome, sepsis for increasing the survival chances in preterm or normal neonates. The supervised learning models like Support Vector Machines(SVM), Decision Trees, K nearest neighbors are found to be effective for neonatal disease detection and will be applied in future research.

scholarly journals Prognosis of Diabetes Mellitus using Machine Learning Techniques

2021 ◽  
Vol 12 (5) ◽  
pp. 836-841
Author(s):  
Vidya J, Swastika T Jain, Shyamala Boosi, Bhanujyothi H C, Dr.Chetana Tukkoji
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Detection Techniques ◽  
End Stage

Diabetes mellitus is a condition caused due to increase in blood glucose level. More than 90% of people are diagnosed with Type 2 diabetes disease,T2D is a fast-growing, chronic disease caused by the imbalance in insulin function. Diabetes is a now the leading cause of heart disease, stroke, blindness, non-traumatic limb amputations and end-stage renal failure. Early detection may take a step towards keeping diabetes patients healthy and it also reduces the risk of such serious complications. Nowadays, the application of Machine learning in the medical field is gradually increasing. This can aid in improving the classification system used for disease diagnosis, that assist medical experts in detecting the fatal diseases at an early stage. This paper presents a performance comparison of the machine learning algorithms in diabetes detection. Techniques like SVM, Random forest, Gradient Boosting, Navie Bayes, Logistic regressionand KNN are used in this work.

Comparative Study of Machine Learning Algorithms on Prediction of Diabetes Mellitus Disease

2020 ◽  
Vol 17 (1) ◽  
pp. 201-205
Author(s):  
Gina George ◽  
Anisha M. Lal ◽  
P. Gayathri ◽  
Niveditha Mahendran
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Ensemble Methods ◽  
Classification Problem ◽  
Disease Diagnosis ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Related Data

Diabetes Mellitus disease is said to occur when there is not proper generation of insulin in the body which is needed for proper regulation of glucose in the body. This health disorder leads to whole degradation of several organs including the heart, kidneys, eyes, nerves. Hence diabetes disease diagnosis by means of accurate prediction is vital. When such disease related data is given as input to several machine learning techniques it becomes an important classification problem. The purpose of the work done in this paper is to compare several classic machine learning algorithms including decision tree, logistic regression and ensemble methods to identify the more accurate classification algorithm for better prediction of the diabetes mellitus disease. This in turn would help for better and effective treatment.

scholarly journals A Machine Learning Approach to Analyse the Symptoms of Covid-19 for the Initial Diagnosis of a Patient

2021 ◽  
pp. 34-40
Author(s):  
K. Anurag Reddy ◽  
Susant Kumar Rath ◽  
Advin Manhar
Keyword(s):  
Machine Learning ◽  
Online Survey ◽  
Learning Algorithm ◽  
Age Groups ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
World Health ◽  
Suspected Infection

The recent outbreak of the respiratory ailment COVID-19 caused by novel corona virus SARS- Cov2 is a severe and urgent global concern. In the absence of vaccine, and also treatment of COVID- 19 WHO (World Health Organization) had informed that Social distancing is the only way to avoid this pandemic and also made clear that Prevention is better than Cure. The main containment strategy is to reduce the contagion by the isolation of affected individuals. Earlier stage this pandemic was declared as a sort of Pneumonia where an individual gets affected by cold, fever and headache. Later, some new symptoms are seen in affected people like sore throat, breathing problems, and sometimes constipation. To make rapid decisions on treatment, and isolation needs, it would be useful to determine which symptoms presented by suspected infection cases are the best predictors of a positive diagnosis. This can be done by analyzing patient's symptoms and its outcome. Here, we developed a model that employed supervised machine learning algorithms to identify the certain features predicting COVID-19 disease diagnosis with high accuracy. Features examined includes details of the concerned individual, e.g., age, gender, observation of fever, breathing difficulty, and clinical details such as the severity of cough and incidence of lung infection and congestion. We had implemented some Machine Learning techniques with algorithms and found out the highest accuracy more than (50 %) of individual patient for all age groups. The following data is collected from COVID-19 positive patients, online survey and social survey done at testing centres. After that we had applied various methods as Data Preprocessing, Model Validation and Statistical analysis, etc. The probability and accuracy of a patient is shown in using various methods of Machine learning algorithm for a better understanding.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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