scholarly journals Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

2022 ◽  
Vol 70 (3) ◽  
pp. 4523-4543
Author(s):  
Ashutosh Kumar Dubey ◽  
Umesh Gupta ◽  
Sonal Jain
Keyword(s):  
Machine Learning ◽  
Data Clustering ◽  
Learning Algorithms ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Clustering And Classification

Data Analytics to Predict, Detect, and Monitor Chronic Autoimmune Diseases Using Machine Learning Algorithms

2021 ◽  
pp. 150-182
Author(s):  
Jayashree M. Kudari
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Huge Data ◽  
Learning Techniques ◽  
Classification And Regression ◽  
Robust Prediction

Developments in machine learning techniques for classification and regression exposed the access of detecting sophisticated patterns from various domain-penetrating data. In biomedical applications, enormous amounts of medical data are produced and collected to predict disease type and stage of the disease. Detection and prediction of diseases, such as diabetes, lung cancer, brain cancer, heart disease, and liver diseases, requires huge tests and that increases the size of patient medical data. Robust prediction of a patient's disease from the huge data set is an important agenda in in this chapter. The challenge of applying a machine learning method is to select the best algorithm within the disease prediction framework. This chapter opts for robust machine learning algorithms for various diseases by using case studies. This usually analyzes each dimension of disease, independently checking the identified value between the limits to monitor the condition of the disease.

scholarly journals Involvement of Machine Learning Tools in Healthcare Decision Making

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Senerath Mudalige Don Alexis Chinthaka Jayatilake ◽  
Gamage Upeksha Ganegoda
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Computational Biology ◽  
Learning Algorithms ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Healthcare Sector ◽  
Accurate Information ◽  
Learning Approaches ◽  
Healthcare Applications

In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans. Implementing algorithms to perform such tasks itself is difficult, but what makes it even more challenging is to increase the accuracy of the algorithm while decreasing the required time for the algorithm to execute. In the early days, processing of large amount of medical data was an important task which resulted in machine learning being adapted in the biological domain. Since this happened, the biology and biomedical fields have been reaching higher levels by exploring more knowledge and identifying relationships which were never observed before. Reaching to its peak now the concern is being diverted towards treating patients not only based on the type of disease but also their genetics, which is known as precision medicine. Modifications in machine learning algorithms are being performed and tested daily to improve the performance of the algorithms in analysing and presenting more accurate information. In the healthcare field, starting from information extraction from medical documents until the prediction or diagnosis of a disease, machine learning has been involved. Medical imaging is a section that was greatly improved with the integration of machine learning algorithms to the field of computational biology. Nowadays, many disease diagnoses are being performed by medical image processing using machine learning algorithms. In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making. Throughout this paper, various machine learning algorithms and approaches that are being used for decision making in the healthcare sector will be discussed along with the involvement of machine learning in healthcare applications in the current context. With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power of modern sophisticated computers and are being extensively applied because of their high predicting accuracy and reliability. When giving concern towards the big picture by combining the observations, it is noticeable that computational biology and biomedicine-based decision making in healthcare have now become dependent on machine learning algorithms, and thus they cannot be separated from the field of artificial intelligence.

scholarly journals Efficient intelligent system for diagnosis pneumonia (SARS-COVID19) in X-Ray images empowered with initial clustering

2021 ◽  
Vol 22 (1) ◽  
pp. 241
Author(s):  
Salam Saad Mohamed Ali ◽  
Ali Hakem Alsaeedi ◽  
Dhiah Al-Shammary ◽  
Hassan Hakem Alsaeedi ◽  
Hadeel Wajeeh Abid
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Clustering ◽  
Intelligent System ◽  
Learning Algorithms ◽  
Image Features ◽  
Machine Learning Algorithms ◽  
Main Role ◽  
Training Process ◽  
X Ray

<span>This paper proposes efficient models to help diagnose respiratory (SARS-COVID19) infections by developing new data descriptors for standard machine learning algorithms using X-Ray images. As COVID-19 is a significantly serious respiratory infection that might lead to losing life, artificial intelligence plays a main role through machine learning algorithms in developing new potential data classification. Data clustering by K-Means is applied in the proposed system advanced to the training process to cluster input records into two clusters with high harmony. Principle Component Analysis PCA, histogram of orientated gradients (HOG) and hybrid PCA and HOG are developed as potential data descriptors. The wrapper model is proposed for detecting the optimal features and applied on both clusters individually. This paper proposes new preprocessed X-Ray images for dataset featurization by PCA and HOG to effectively extract X-Ray image features. The proposed systems have potentially empowered machine learning algorithms to diagnose Pneumonia (SARS-COVID19) with accuracy up to %97.</span>

scholarly journals Prediction of Heart Disease Using Machine Learning Algorithms

2021 ◽  
Vol 36 (1) ◽  
pp. 260-264
Author(s):  
S. Ravi ◽  
Dr.M. Sambath ◽  
Dr.J. Thangakumar ◽  
Danam Kumar ◽  
Gorantla Naveen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithms ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Hospital Treatment ◽  
Learning Techniques ◽  
Automate Machine ◽  
Disease Hospital

As big data becomes more prevalent in the healthcare and medical sectors, accurate medical data collection benefits early diagnosis of heart disease, hospital treatment, and government resources. However, where medical data quality is lacking, understanding accuracy suffers. Consequently, some field diseases have unique features in different regions, which can make illness more difficult. It is now more hard to predict outbreaks. We automate machine learning algorithms for efficient epidemic detection in bacterial infection population in this paper. We put the modified forecasts to the test using securely and efficiently datasets. areas of the region to improve the situation of lost data, we use a predictive modeling approach to restore inaccurate value. Focused upon its patient's signs, a heart attack is suspected. Models were built using machine learning techniques. As a consequence, the accuracy is pinpoint accurate. The Flask web interface is used to build the Application. In this research, we shall conduct experiments using machine learning methods.

MLkit: A machine-learning-powered automatic workflow for classification of cancer samples.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13583-e13583
Author(s):  
Qingyuan Li ◽  
Ji He
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Cancer Patients ◽  
Learning Algorithms ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
One Stop ◽  
Tissue Of Origin

e13583 Background: In the era of data explosion, precision classification of cancer samples based on multi-dimensional medical data provides more insights into disease mechanism and useful hints on clinical treatment associated with tissue of origin, recurrence tendency and prognostic of chemotherapy or immunotherapy. We developed an automatic workflow MLkit to select features from large-scale multi-dimensional medical data and conduct classification through various machine learning techniques. Methods: MLkit is an automatic and one-stop workflow for classification of cancer samples with four modules: preprocessing (missing data remove or imputation and feature standardization), feature selection (unsupervised multi-statistics and supervised multiple machine estimators with recursive feature elimination and cross-validation), modeling (hyper-parameter, performance evaluation and probability calibration) and prediction. Most of current machine learning algorithms were implemented in this workflow, including linear model (logistic regression, ridge regression and stochastic gradient descent), ensemble model (gradient boosting, random forest, xgboost, catboost, lightgmb and stacking), support vector kernel (linear and non-linear), naive Bayes, k-nearest neighbors and multi-layer perceptron neural network. To evaluate the performance of this workflow, we utilized it to fit a model used for prediction of tissue of origin based on 450K DNA methylation data of 2,210 samples from lung, kidney and breast cancer patients collected in TCGA. Results: MLkit performed well in the prediction of tissue of origin for independent validation sets of cancer patients with stable feature selection, automatic hyper-parameters and efficient probability calibration, in which the model achieved AUCs ranged from 0.85 to 0.96. In addition, we also utilized this workflow on extensive real world data and most of results showed superior accuracy and stable performance. Conclusions: MLkit facilitates automated and one-stop classification of cancer samples using machine learning algorithms. It can be operated with simple command line, making it accessible to a broad range of users. The well performance of this workflow based on multi-dimensional medical data can help to improve the discovery of tumor biomarker and optimize clinical follow-up and therapeutic treatment for cancer patients.

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