scholarly journals Early Stage Identification of COVID-19 Patients in Mexico Using Machine Learning: A Case Study for the Tijuana General Hospital

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 490
Author(s):  
Cristián Castillo-Olea ◽  
Roberto Conte-Galván ◽  
Clemente Zuñiga ◽  
Alexandra Siono ◽  
Angelica Huerta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mathematical Model ◽  
Logistic Regression ◽  
General Hospital ◽  
Early Stage ◽  
Individual Characteristics ◽  
Supervised Learning Algorithms ◽  
Neural Network Algorithm ◽  
The Individual ◽  
Logistic Regression Algorithm

Background: The current pandemic caused by SARS-CoV-2 is an acute illness of global concern. SARS-CoV-2 is an infectious disease caused by a recently discovered coronavirus. Most people who get sick from COVID-19 experience either mild, moderate, or severe symptoms. In order to help make quick decisions regarding treatment and isolation needs, it is useful to determine which significant variables indicate infection cases in the population served by the Tijuana General Hospital (Hospital General de Tijuana). An Artificial Intelligence (Machine Learning) mathematical model was developed in order to identify early-stage significant variables in COVID-19 patients. Methods: The individual characteristics of the study subjects included age, gender, age group, symptoms, comorbidities, diagnosis, and outcomes. A mathematical model that uses supervised learning algorithms, allowing the identification of the significant variables that predict the diagnosis of COVID-19 with high precision, was developed. Results: Automatic algorithms were used to analyze the data: for Systolic Arterial Hypertension (SAH), the Logistic Regression algorithm showed results of 91.0% in area under ROC (AUC), 80% accuracy (CA), 80% F1 and 80% Recall, and 80.1% precision for the selected variables, while for Diabetes Mellitus (DM) with the Logistic Regression algorithm it obtained 91.2% AUC, 89.2% accuracy, 88.8% F1, 89.7% precision, and 89.2% recall for the selected variables. The neural network algorithm showed better results for patients with Obesity, obtaining 83.4% AUC, 91.4% accuracy, 89.9% F1, 90.6% precision, and 91.4% recall. Conclusions: Statistical analyses revealed that the significant predictive symptoms in patients with SAH, DM, and Obesity were more substantial in fatigue and myalgias/arthralgias. In contrast, the third dominant symptom in people with SAH and DM was odynophagia.

scholarly journals Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) For Data Privacy in the Context of Big Data

2021 ◽  
Vol 2083 (3) ◽  
pp. 032059
Author(s):  
Qiang Chen ◽  
Meiling Deng
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Privacy Protection ◽  
Data Privacy ◽  
Absolute Error ◽  
Average Absolute Error ◽  
Regression Algorithms ◽  
Hadoop Platform ◽  
Logistic Regression Algorithm ◽  
Computing Speed

Abstract Regression algorithms are commonly used in machine learning. Based on encryption and privacy protection methods, the current key hot technology regression algorithm and the same encryption technology are studied. This paper proposes a PPLAR based algorithm. The correlation between data items is obtained by logistic regression formula. The algorithm is distributed and parallelized on Hadoop platform to improve the computing speed of the cluster while ensuring the average absolute error of the algorithm.

Using a Machine Learning Logistic Regression Algorithm to Classify Nanomedicine Clinical Trials in a Known Repository

2019 ◽  
pp. 98-110
Author(s):  
Charles M. Pérez-Espinoza ◽  
Nuvia Beltran-Robayo ◽  
Teresa Samaniego-Cobos ◽  
Abel Alarcón-Salvatierra ◽  
Ana Rodriguez-Mendez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Logistic Regression ◽  
Logistic Regression Algorithm

scholarly journals Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients

2020 ◽  
Vol 10 (11) ◽  
pp. 764
Author(s):  
Kyeong-Rae Kim ◽  
Hyeun Sung Kim ◽  
Jae-Eun Park ◽  
Seung-Yeon Kang ◽  
So-Young Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spinal Stenosis ◽  
Area Under The Curve ◽  
Individual Characteristics ◽  
Learning Technology ◽  
Short Term ◽  
Korean Patients ◽  
Patient Prognosis ◽  
Prognostic Prediction ◽  
The Individual

Background: In this study, based on machine-learning technology, we aim to develop a predictive model of the short-term prognosis of Korean patients who received spinal stenosis surgery. Methods: Using the data obtained from 112 patients with spinal stenosis admitted at N hospital from February to November, 2019, a predictive analysis was conducted for the pain index, reoperation, and surgery time. Results: Results show that the predicted area under the curve was 0.803, 0.887, and 0.896 for the pain index, reoperation, and surgery time, respectively, thereby indicating the accuracy of the model. Conclusion: This study verified that the individual characteristics of the patient and treatment characteristics during surgery enable a prediction of the patient prognosis and validate the accuracy of the approach. Further studies should be conducted to extend the scope of this research by incorporating a larger and more accurate dataset.

scholarly journals Deep Learning Technique to Predict Heart Disease using IoT Based ECG Data

2019 ◽  
Vol 9 (2) ◽  
pp. 2559-2562
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Learning Techniques ◽  
Learning Technique ◽  
Logistic Regression Algorithm ◽  
Target Output

Scientific Knowledge and Electronic devices are growing day by day. In this aspect, many expert systems are involved in the healthcare industry using machine learning algorithms. Deep neural networks beat the machine learning techniques and often take raw data i.e., unrefined data to calculate the target output. Deep learning or feature learning is used to focus on features which is very important and gives a complete understanding of the model generated. Existing methodology used data mining technique like rule based classification algorithm and machine learning algorithm like hybrid logistic regression algorithm to preprocess data and extract meaningful insights of data. This is, however a supervised data. The proposed work is based on unsupervised data that is there is no labelled data and deep neural techniques is deployed to get the target output. Machine learning algorithms are compared with proposed deep learning techniques using TensorFlow and Keras in the aspect of accuracy. Deep learning methodology outfits the existing rule based classification and hybrid logistic regression algorithm in terms of accuracy. The designed methodology is tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal beats. The proposed approach based on deep learning technique offered a better performance, improving the results when compared to machine learning approaches of the state-of-the-art

scholarly journals Data Mining Technique for Diabetes Diagnosis using Classification Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 9044-9049
Keyword(s):  
Logistic Regression ◽  
Early Stage ◽  
Classification Algorithms ◽  
Diabetes Diagnosis ◽  
Correct Classification ◽  
Data Mining Technique ◽  
Receiver Operating Characteristic Curves ◽  
Logistic Regression Algorithm ◽  
High Level

Diabetes mellitus is defined as a one of the chronic and deadliest diseases which combined with abnormally high level of sugar (glucose) in the blood. The classification technique helps in diagnosis the symptoms at starting stages. This paper focused to prognosticate the chance of diabetes in patients with extremely correct classification of Diabetes. The classification algorithms viz., Naïve Bayes, Logistic Regression, and Decision Tree can be used to detect diabetes at an early stage. The algorithm performances are evaluated based on various measures like Recall, Precision, and F-Measure. Experiments are conducted where the time complexity of each of the algorithm is measured. Accuracy is also measured over correct classification and misclassification instances, observed that a Logistic Regression algorithm has much better performance when compared to the other type classifications. Using Receiver Operating Characteristic curves the results are verified in a systematic manner.

scholarly journals Prediction of Cardiovascular Disease using Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 2404-2414
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Human Life ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Noise Data ◽  
The Individual

Background/Aim: Healthcare is an unavoidable assignment to be done in human life. Cardiovascular sickness is a general class for a scope of infections that are influencing heart and veins. The early strategies for estimating the cardiovascular sicknesses helped in settling on choices about the progressions to have happened in high-chance patients which brought about the decrease of their dangers. Methods: In the proposed research, we have considered informational collection from kaggle and it doesn't require information pre-handling systems like the expulsion of noise data, evacuation of missing information, filling default esteems if applicable and classification of attributes for prediction and decision making at different levels. The performance of the diagnosis model is obtained by using methods like classification, accuracy, sensitivity and specificity analysis. This paper proposes a prediction model to predict whether a people have a cardiovascular disease or not and to provide an awareness or diagnosis on that. This is done by comparing the accuracies of applying rules to the individual results of Support Vector Machine, Random forest, Naive Bayes classifier and logistic regression on the dataset taken in a region to present an accurate model of predicting cardiovascular disease. Results: The machine learning algorithms under study were able to predict cardiovascular disease in patients with accuracy between 58.71% and 77.06%. Conclusions: It was shown that Logistic Regression has better Accuracy (77.06 %) when compared to different Machine-learning Algorithms.

scholarly journals Disease Prediction for the Deprived using Machine Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 404-408
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Chronic Disease ◽  
Social Welfare ◽  
Early Stage ◽  
Disease Prediction ◽  
Disease Diagnostics ◽  
The One ◽  
The Cost ◽  
Deficiency Diseases

Our work aims for economical disease diagnostics, by asking the user for Prognosis and symptoms, accurate disease prediction has been strived for. In aspiration for social welfare, the cost of using the product built is almost free, the prediction can be done using any one of the six algorithms, five out of which are total free of cost for use, those five being KNN, Naïve Bayes, SVM , Logistic Regression, K Means Classifier. The one, that gives out predictions with most accuracy, i.e., Decision Trees Classifier, has been made paid, others are not to be paid for, for using.How this product would be functioning is simple: User logs in , openCV has been used for it, that brings the user to the section where user is briefed about models working on different algorithms, each algorithm having different accuracy, thus further, which model he/ she should choose. On choosing model of their choice, they fill their symptoms and prognosis, that yields them their final result of name of their disease.Services like these are greatly needed , looking at large many number of people in our society, who are unfortunately not able to afford them, when priced heavily, or even moderately. Such products can help save many a lives, notify sufferer about his chronic disease at early stage, inform about deficiency diseases, that are very controllable, if get known about, early.

scholarly journals Machine Learning Algorithm’s Measurement and Analytical Visualization of User’s Reviews for Google Play Store

2020 ◽  
Author(s):  
Abdul Karim ◽  
Azhari Azhari ◽  
Samir Brahim Belhaouri ◽  
Ali Adil Qureshi
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Ve Bayes ◽  
Android Apps ◽  
Online Marketplace ◽  
Document Frequency ◽  
Logistic Regression Algorithm ◽  
Google Play

The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps’ reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Naïve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.

scholarly journals Application of the Preoperative Assistant System Based on Machine Learning in Hepatocellular Carcinoma Resection

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Shouyun Lv ◽  
Shizong Li ◽  
Zhiwei Yu ◽  
Kaiqiong Wang ◽  
Xin Qiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Liver Volume ◽  
Surgical Margin ◽  
Extensive Hepatectomy ◽  
Significant Difference ◽  
Logistic Regression Algorithm

To conduct better research in hepatocellular carcinoma resection, this paper used 3D machine learning and logistic regression algorithm to study the preoperative assistance of patients undergoing hepatectomy. In this study, the logistic regression model was analyzed to find the influencing factors for the survival and recurrence of patients. The clinical data of 50 HCC patients who underwent extensive hepatectomy (≥4 segments of the liver) admitted to our hospital from June 2020 to December 2020 were selected to calculate the liver volume, simulated surgical resection volume, residual liver volume, surgical margin, etc. The results showed that the simulated liver volume of 50 patients was 845.2 + 285.5 mL, and the actual liver volume of 50 patients was 826.3 ± 268.1 mL, and there was no significant difference between the two groups (t = 0.425; P  > 0.05). Compared with the logistic regression model, the machine learning method has a better prediction effect, but the logistic regression model has better interpretability. The analysis of the relationship between the liver tumour and hepatic vessels in practical problems has specific clinical application value for accurately evaluating the volume of liver resection and surgical margin.

scholarly journals Prediction of Prostate Cancer using Machine Learning Algorithms

2020 ◽  
Vol 8 (5) ◽  
pp. 5353-5362
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Missing Values ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Cancer Disease ◽  
The Individual

Background/Aim: Prostate cancer is regarded as the most prevalent cancer in the word and the main cause of deaths worldwide. The early strategies for estimating the prostate cancer sicknesses helped in settling on choices about the progressions to have happened in high-chance patients which brought about the decrease of their dangers. Methods: In the proposed research, we have considered informational collection from kaggle and we have done pre-processing tasks for missing values .We have three missing data values in compactness attribute and two missing values in fractal dimension were replaced by mean of their column values .The performance of the diagnosis model is obtained by using methods like classification, accuracy, sensitivity and specificity analysis. This paper proposes a prediction model to predict whether a people have a prostate cancer disease or not and to provide an awareness or diagnosis on that. This is done by comparing the accuracies of applying rules to the individual results of Support Vector Machine, Random forest, Naive Bayes classifier and logistic regression on the dataset taken in a region to present an accurate model of predicting prostate cancer disease. Results: The machine learning algorithms under study were able to predict prostate cancer disease in patients with accuracy between 70% and 90%. Conclusions: It was shown that Logistic Regression and Random Forest both has better Accuracy (90%) when compared to different Machine-learning Algorithms.

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