scholarly journals Kecenderungan #DiRumahSaja dan Perilaku Adaptif dalam Melawan Covid-19

2021 ◽  
Vol 7 (2) ◽  
pp. 131
Author(s):  
Tabah Aris Nurjaman
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Model ◽  
Machine Learning Model

Wabah Covid-19 telah merenggut banyak nyawa hingga saat ini, sehingga menuntut kesadaran kita untuk berusaha bersama-sama melakukan tindakan kolektif dalam bentuk apapun. Penelitian ini bertujuan untuk mengeksplorasi perilaku adaptif dalam melawan Covid-19 dan memprediksi kecenderungan untuk berdiam di tempat tinggal atau #dirumahsaja berdasarkan jenis kelamin, masa perkembangan, dan skema psikologis (kognisi, afeksi, dan perilaku). Tahap eksplorasi perilaku adaptif dilakukan dengan menggunakan pendekatan psikologi indigenous. Tahap prediksi kecenderungan #dirumahsaja dilakukan dengan implementasi machine learning model decision tree. Pengambilan data dilakukan dalam satu waktu dengan melibatkan 272 responden (67,6% wanita; Musia=24,4; SDusia=10,38). Berdasarkan hasil eksplorasi, penyebaran Covid-19 menjadi tanggung jawab diri sendiri (67,65%), pemangku kebijakan (22,79%), dan objek lain (9,56%). Kondisi afeksi dalam menyikapi Covid-19 didominasi oleh perasaan cemas (47,79%), diikuti usaha tetap tenang (32,72%) dan perasaan sedih (19,47%). Perilaku adaptif ditunjukkan dengan memutus rantai penyebaran virus (45,22%), menjaga pola hidup bersih dan sehat (42,28%), dan mengikuti anjuran pemerintah (12,5%). Berdasarkan model decision tree, kecenderungan #dirumahsaja terjadi pada wanita dewasa awal dan dewasa madya yang merasa cemas akan Covid-19 dan berperilaku adaptif dengan memutus rantai penyebaran virus.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

scholarly journals A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yi Yin ◽  
Mingyue Xue ◽  
Lingen Shi ◽  
Tao Qiu ◽  
Derun Xia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Decision Tree ◽  
Learning Model ◽  
Heterosexual Transmission ◽  
Adaboost Algorithm ◽  
Machine Learning Model ◽  
B Virus ◽  
Homosexual Transmission

Objective. To establish a machine learning model for identifying patients coinfected with hepatitis B virus (HBV) and human immunodeficiency virus (HIV) through two sexual transmission routes in Jiangsu, China. Methods. A total of 14197 HIV cases transmitted by homosexual and heterosexual routes were recruited. After data processing, 12469 cases (HIV and HBV, 1033; HIV, 11436) were left for further analysis, including 7849 cases with homosexual transmission and 4620 cases with heterosexual transmission. Univariate logistic regression was used to select variables with significant P value and odds ratio for multivariable analysis. In homosexual transmission and heterosexual transmission groups, 10 and 6 variables were selected, respectively. For identifying HIV individuals coinfected with HBV, a machine learning model was constructed with four algorithms, including Decision Tree, Random Forest, AdaBoost with decision tree (AdaBoost), and extreme gradient boosting decision tree (XGBoost). The detective value of each variable was calculated using the optimal machine learning algorithm. Results. AdaBoost algorithm showed the highest efficiency in both transmission groups (homosexual transmission group: accuracy = 0.928 , precision = 0.915 , recall = 0.944 , F − 1 = 0.930 , and AUC = 0.96 ; heterosexual transmission group: accuracy = 0.892 , precision = 0.881 , recall = 0.905 , F − 1 = 0.893 , and AUC = 0.98 ). Calculated by AdaBoost algorithm, the detective value of PLA was the highest in homosexual transmission group, followed by CR, AST, HB, ALT, TBIL, leucocyte, age, marital status, and treatment condition; in the heterosexual transmission group, the detective value of PLA was the highest (consistent with the condition in the homosexual group), followed by ALT, AST, TBIL, leucocyte, and symptom severity. Conclusions. The univariate logistics regression combined with the AdaBoost algorithm could accurately screen the risk factors of HBV in HIV coinfection without invasive testing. Further studies are needed to evaluate the utility and feasibility of this model in various settings.

scholarly journals A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

2020 ◽  
Author(s):  
Qiao Yang ◽  
Jixi Li ◽  
Zhijia Zhang ◽  
Xiaocheng Wu ◽  
Tongquan Liao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Area Under The Curve ◽  
Lactic Dehydrogenase ◽  
Learning Model ◽  
Severe Illness ◽  
Decision Tree Classifier ◽  
Risk Of Death ◽  
Machine Learning Model ◽  
Tree Classifier

Abstract BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.MethodsA total of 2,169 adult COVID-19 patients were enrolled from Wuhan, China between February 10th and April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups as well as between survivors and non-survivors. In addition, we developed a decision tree classifier to identify risk factors for death outcome.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. The most common system symptoms were respiratory, systemic and digestive symptoms. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A machine learning model was developed to predict death outcome in severe patients. The decision tree classifier included three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase. The area under the curve of the receiver operating characteristic of this model was 0.96. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

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