Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Prediction of cyanotic and acyanotic congenital heart disease using machine learning models

BACKGROUND Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.AIM To identify and build the best predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their potential risk factors.METHODS The data were collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan,Pakistan from December 2017 to October 2019.A sample of 3900 mothers whose children were diagnosed with identify the potential outliers.Different machine learning models were compared,and the best-fitted model was selected using the area under the curve,sensitivity,and specificity of the models.RESULTS Out of 3900 patients included,about 69.5%had acyanotic and 30.5%had cyanotic congenital heart disease.Males had more cases of acyanotic(53.6%)and cyanotic(54.5%)congenital heart disease as compared to females.The odds of having cyanotic was 1.28 times higher for children whose mothers used more fast food frequently during pregnancy.The artificial neural network model was selected as the best predictive model with an area under the curve of 0.9012,sensitivity of 65.76%,and specificity of 97.23%.CONCLUSION Children having a positive family history are at very high risk of having cyanotic and acyanotic congenital heart disease.Males are more at risk and their mothers need more care,good food,and physical activity during pregnancy.The best-fitted model for predicting cyanotic and acyanotic congenital heart disease is the artificial neural network.The results obtained and the best model identified will be useful for medical practitioners and public health scientists for an informed decision-making process about the earlier diagnosis and improve the health condition of children in Pakistan.

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.

A New Aware-Context Collaborative Filtering Approach by Applying Multivariate Logistic Regression Model into General User Pattern

Traditional collaborative filtering (CF) does not take into account contextual factors such as time, place, companion, environment, etc. which are useful information around users or relevant to recommender application. So, recent aware-context CF takes advantages of such information in order to improve the quality of recommendation. There are three main aware-context approaches: contextual pre-filtering, contextual post-filtering and contextual modeling. Each approach has individual strong points and drawbacks but there is a requirement of steady and fast inference model which supports the aware-context recommendation process. This paper proposes a new approach which discovers multivariate logistic regression model by mining both traditional rating data and contextual data. Logistic model is optimal inference model in response to the binary question “whether or not a user prefers a list of recommendations with regard to contextual condition”. Consequently, such regression model is used as a filter to remove irrelevant items from recommendations. The final list is the best recommendations to be given to users under contextual information. Moreover the searching items space of logistic model is reduced to smaller set of items so-called general user pattern (GUP). GUP supports logistic model to be faster in real-time response.

Analysis of gender＇s role on voluntary tendency of potential/active volunteers via logistic regression modeling： The case of Canakkale Onsekiz Mart University

From economy to political administrations, education to health, environment to human rights, many problems we met have gained a global importance in recent days. Existing state systems, political parties and nation states are not adequate for solving these problems in question effectively on their own. Not only governments and local authorities but also voluntary organizations based on completely voluntary activities have significant roles in solving these problems. Effective performance of voluntary organizations depends on increasing volunteer population. Individuals＇ attitudes or their perception of understanding volunteerism play an important role in their contributions to voluntary organizations. The aim of this study is to determine individuals＇ ways of perceiving volunteerism concept and their tendency towards it. Furthermore, differences between men and women＇s perception and attitudes towards volunteerism concept have been examined. For this purpose, a survey has been conducted over university students of bachelor＇s degree. Tendencies and attitudes towards volunteerism compared to gender differences have been tested via logistic regression method. Research results reveal that women take part in voluntary activities more than men and women perceive volunteerism as ＂a political position＂ while men perceive volunteerism as ＂a learning atmosphere and learning process＂.

Landslide-Dammed Mapping and Logistic Regression Modeling Using GIS and R Statistical Software in the Northeast Afghanistan

A complex terrain and topography resulted in an enormous landslide-dammed area northeast of Afghanistan. Moreover, debris, rock avalanches, and landslides occurrences are the primary source of lakes created within the area. Recently, instances have increased because of the high displacement and mass movement by glacial and seismic activities. In this study, using GIS and R statistical software, we performed a logistic regression modeling in order to map and predict the probability of landslides-dammed occurrences. Totally, 361 lakes were mapped using Google Earth historical imagery. This total was divided into 253 （70%） lakes for modeling and 801 （30%） lakes for the model validation. They were randomly selected by creating a fishnet for the study area using Arc toolbox in GIS. Four independent variables that are mostly contributed to landslide-dammed occurrences consisting of slope angles, relief classes, distances to major water sources and earthquake epicenters, were extracted from DEM （digital elevation model） data using 85-meter resolution. The result is a grid map that classified the area into Low （16,834.98 km2）, Medium （2,217.302 kin：） and High （2,013.55 km2） vulnerability to landslide-dammed occurrences. Overall, the model result has been validated by using a ROC （receiver operator characteristic） curve available in SPSS software. The model validation showed a 95.1 percent prediction accuracy that is considered satisfactory.

Development and application of hepatocellular carcinoma risk prediction model based on clinical characteristics and liver related indexes

BACKGROUND Hepatocellular carcinoma(HCC)is difficult to diagnose with poor therapeutic effect,high recurrence rate and has a low survival rate.The survival of patients with HCC is closely related to the stage of diagnosis.At present,no specific serolo-gical indicator or method to predict HCC,early diagnosis of HCC remains a challenge,especially in China,where the situation is more severe.AIM To identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators.METHODS The clinical data of patients in the Affiliated Hospital of North Sichuan Medical College from 2016 to 2020 were collected,using a retrospective study method.The results of needle biopsy or surgical pathology were used as the grouping criteria for the experimental group and the control group in this study.Based on the time of admission,the cases were divided into training cohort(n=1739)and validation cohort(n=467).Using HCC as a dependent variable,the research indicators were incorporated into logistic univariate and multivariate analysis.An HCC risk prediction model,which was called NSMC-HCC model,was then established in training cohort and verified in validation cohort.RESULTS Logistic univariate analysis showed that,gender,age,alpha-fetoprotein,and protein induced by vitamin K absence or antagonist-II,gamma-glutamyl transferase,aspartate aminotransferase and hepatitis B surface antigen were risk factors for HCC,alanine aminotransferase,total bilirubin and total bile acid were protective factors for HCC.When the cut-off value of the NSMC-HCC model joint prediction was 0.22,the area under receiver operating characteristic curve(AUC)of NSMC-HCC model in HCC diagnosis was 0.960,with sensitivity 94.40%and specificity 95.35%in training cohort,and AUC was 0.966,with sensitivity 90.00%and specificity 94.20%in validation cohort.In early-stage HCC diagnosis,the AUC of NSMC-HCC model was 0.946,with sensitivity 85.93%and specificity 93.62%in training cohort,and AUC was 0.947,with sensitivity 89.10%and specificity 98.49%in validation cohort.CONCLUSION The newly NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.

To set up a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on the efficacy of Chinese herbal medicines

In our previous research,a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on the four properties,five flavors and channel tropism has been successfully established.However,could Chinese herbal medicines efficacy also be applied to predict the hepatotoxicity of Chinese herbal medicines?Therefore,a logistic regression prediction model for hepatotoxicity of Chinese herbal medicines based on Chinese herbal medicines efficacy has been tentatively set up to study the correlations of hepatotoxic and nonhepatotoxic Chinese herbal medicines with efficacy by using a chi-square test for two-way unordered categorical data.Logistic regression prediction model was established and the accuracy of the prediction by this model was evaluated.It has been found that the hepatotoxicity and nonhepatotoxicity of Chinese herbal medicines were weakly related to the efficacy,and the coefficient was 0.295.There were 20 variables from Chinese herbal medicines efficacy analyzed with unconditional logistic regression,and 6 variables,rectifying Qi and relieving pain,clearing heat and disinhibiting dampness,invigorating blood and stopping pain,invigorating blood and relieving swelling,killing worms and relieving fright were chosen to establish the logistic regression prediction model,with the optimal cutoff value being 0.250.Dissipating cold and relieving pain(DCRP),clearing heat and disinhibiting dampness,invigorating blood and relieving pain(IBRP),invigorating blood and relieving swelling,killing worms,and relieving fright were the variables to affect the hepatotoxicity and the established logistic regression prediction model had predictive power for hepatotoxicity of Chinese herbal medicines to a certain degree.

GIS-based evaluation of landslide susceptibility using a novel hybrid computational intelligence model on different mapping units

Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid model,namely box counting dimension-based kernel logistic regression model,which uses fractal dimension calculated by box counting method as input data based on grid cells mapping unit and terrain mapping unit.The performance of this model was evaluated in the application in Zhidan County,Shaanxi Province,China.Firstly,a total of 221 landslides were identified and mapped,and 11 landslide predisposing factors were considered.Secondly,the landslide susceptibility maps(LSMs) of the study area were obtained by constructing the model on two different mapping units.Finally,the results were evaluated with five statistical indexes,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV) and Accuracy.The statistical indexes of the model obtained on the terrain mapping unit were larger than those based on grid cells mapping unit.For training and validation datasets,the area under the receiver operating characteristic curve(AUC) of the model based on terrain mapping unit were 0.9374 and 0.9527,respectively,indicating that establishing this model on the terrain mapping unit was advantageous in the study area.The results show that the fractal dimension improves the prediction ability of the kernel logistic model.In addition,the terrain mapping unit is a more promising mapping unit in Loess areas.

A COMPARISON OF FORECASTING MODELS OF THE VOLATILITY IN SHENZHEN STOCK MARKET

Based on the weekly closing price of Shenzhen Integrated Index, this article studies the volatility of Shenzhen Stock Market using three different models： Logistic, AR（1） and AR（2）. The time-variable parameters of Logistic regression model is estimated by using both the index smoothing method and the time-variable parameter estimation method. And both the AR（1） model and the AR（2） model of zero-mean series of the weekly dosing price and its zero-mean series of volatility rate are established based on the analysis results of zero-mean series of the weekly closing price, Six common statistical methods for error prediction are used to test the predicting results. These methods are： mean error （ME）, mean absolute error （MAE）, root mean squared error （RMSE）, mean absolute percentage error （MAPE）, Akaike＇s information criterion （AIC）, and Bayesian information criterion （BIC）. The investigation shows that AR（1） model exhibits the best predicting result, whereas AR（2） model exhibits predicting results that is intermediate between AR（1） model and the Logistic regression model.

Stochastic Modeling for Coliform Count Assessment in Ground Water

Stochastic models are derived to estimate the level of coliform count in terms of MPN index, one of the most important water quality characteristic in ground water based on a set of water source location and soil characteristics. The study is based on about twenty location and soil characteristics, majority of them are observed through laboratory analysis of soil and water samples collected from nearly thee hundred locations of drinking water sources, wells and bore wells selected at random from the district of Kasaragod. The water contamination in wells are found to be relatively more as compared to bore wells. The study reveals that only 7 % of the wells and 40 o~ of the bore wells of the district are within the permissible limit of WHO standard of drinking water quality. The level of contamination is very high in the hospital premises and is very low in the forest area. Two separate multiple ordinal logistic regression models are developed to predict the level of coliform count, one for well and the other for bore well. The significant feature of this study is that in addition to scientifically proving the dependence of the water quality on the distances from waste disposal area and septic tanks etc., it highlights the dependence of two other very significant soil characteristics, the soil organic carbon and soil porosity. The models enable to predict the quality of water in a location based on the set of soil and location characteristics. One of the important uses of the model is in fixing safe locations for waste dump area, septic tank, digging well etc. in town planning, designing residential layouts, industrial layouts, hospital/hostel construction etc. This is the first ever study to describe the ground water quality in terms of the location and soil characteristics.

Impact of forest governance and enforcement on deforestation and forest degradation at the district level:A study in West Bengal State,India

According to the United Nations Environmental Programme(UNEP),the world loses 1.0×106hm2forest land through deforestation annually.About 1.6×106people who depend on forests for livelihood are negatively affected by deforestation and forest degradation.The paper attempts to study the impact of forest governance,enforcement and socio-economic factors on deforestation and forest degradation at the local level in West Bengal State,India.The study was based on questionnaire survey data during 2020–2021 collected from three western districts(Purulia,Bankura,and Paschim Medinipur)where deforestation and poverty rates are higher than other districts in West Bengal State.The total number of selected villages was 29,and the total sample households were 693.A stratified random sampling technique was used to collect data,and a questionnaire was followed.Forest governance and enforcement indices were constructed using United Nation Development Programme(UNDP)methodology and a step-wise logistic regression model was used to identify the factors affecting deforestation and forest degradation.The result of this study showed that four factors(illegal logging,weak forest administration,encroachment,and poverty)are identified for the causes of deforestation and forest degradation.It is observed that six indices of forest governance(rule of law,transparency,accountability,participation,inclusiveness and equitability,and efficiency and effectiveness)are relatively high in Purulia District.Moreover,this study shows that Purulia and Bankura districts follow medium forest governance,while Paschim Medinipur District has poor forest governance.The enforcement index is found to be highest in Purulia District(0.717)and lowest for Paschim Medinipur District(0.257).Finally,weak forest governance,poor socio-economic conditions of the households,and weak enforcement lead to the deforestation and forest degradation in the study area.Therefore,governments should strengthen law enforcement and encourage sustainable forest certification schemes to combat illegal logging.

Analysis of Lymph Node Metastases of 1,526 Cases with Thoracic Esophageal and Cardiac Carcinomas: A Random Sampling Report froni the Fourth Hospital of Hebei Medical University from 1996 to 2004

OBJECTIVE To summarize the regular pattern and state oflymph node metastasis of patients with esophageal and cardiaccarcinomas,so as to analyze factors influencing lymph nodemetastasis.METHODS Clinical data collected from 1,526 thoracicesophageal and cardiac carcinoma patients who were admitted inthe Fourth Hospital of Hebei Medical University during a periodfrom January 1996 to December 2004,were randomly selectedand an Access Database of the patient's information was set up.Eight clinico-pathologic factors,including the patient's age,tumorlocation and size,pathological classification,the depth of tumorinvasion,vascular tumor embolus (VTE),the state of surroundingorgan encroachment and the status of tumor residues,wereidentified.A correlation between these factors and metastases wasstatistically analyzed using SPSS13.0 software.RESULTS Lymph node metastatic sites from esophagealcarcinomas included the thoracic and abdominal cavity.Lymphnode metastasis from the superior esophageal carcinomasmainly occurred in the neck and thoracic cavity.There was atwo-way lymph node metastasis in the patients with the middleesophageal carcinoma.The inferior esophageal carcinomas mainlymetastasized to the paraesophageal,paragastric cardia,and leftgastric artery lymph nodes.The rate and degree of the metastasisfrom the inferior esophageal carcinomas were significantly highercompared to those of the superior and the middle esophagealcarcinomas (P<0.0125).The degree of abdominal lymph node metastasis fromcarcinomas of the gastric cardia was significantly higher comparedwith that of esophageal carcinomas.In the group with carcinomaof the gastric cardia,the rate and degree of the lymph nodemetastases in the paragastric cardia and left gastric artery weresignificantly higher compared to the group with esophagealcarcinoma (P<0.05).Paraesophageal lymph node metastasis fromcarcinomas of the gastric cardia in the thoracic cavity frequentlyoccurred,too,and the degree of the metastasis was similar to thatof esophageal carcinoma.There was no significant difference inthe rate and degree of the paraesophageal lymph-node metastasisbetween the group with carcinoma of the gastric cardia comparedto those with esophageal carcinoma (P>0.05).Multifactoriallogistic regression analysis showed that the tumor size,depth oftumor encroachment,VTE,and tumor residues could all bringabout obvious impact on lymph-node metastases (P<0.05).CONCLUSION Lymph node metastasis from superioresophageal carcinomas mainly occurs in the neck and thoraciccavity.The middle esophageal carcinomas presented a two-waylymph-node metastasis (both the upwards and the downwards),and the lymph node metastasis from inferior esophagealcarcinomas mainly occurred in the thoracic and abdominal cavities.The metastases of carcinoma of the gastriccardia were most commonly found in the abdominalcavity,with frequent paraesophageal lymph-nodemetastasis.The sufficient attention should be paidto neck lymph node clearance in cases of esophagealcarcinoma.What is of the greatest concern is theclearance of the left gastric artery lymph nodes,andalso in cases of gastric cardia carcinoma,clearance,the paraesophageal lymph nodes.With an increasein the tumor size and depth of tumor encroachment,and occurrence of VTE and tumor residual cells,therisk of lymph node metastasis is significantly raised (P<0.05).

An In-Depth Analysis of the Causes of Road Accidents in Developing Countries: Case Study of Douala-Dschang Highway in Cameroon

This paper is aimed at identifying the risk factors that mainly contribute to reckless driving and other related causes of road accidents along the Douala-Dschang highway of Cameroon. The research work started with the collection of accident reports for 2018 and 2019 from security officials in charge of road safety and the police stations of the different localities included in the sample of the study. Three hundred and eighty-two (382) road accidents reports were collected and analyzed using the 2020 version logit regression model of XLSTAT. From these analyses, it appears that, of the 382 accidents recorded during this period, six factors were identified and classified as follows: causes of accidents related to speed and carelessness, location of the accident, type of vehicle at fault, day the accident occurred, time of the accident and the age of drivers involved. These results could contribute to reduce the gravity of accidents along the Douala-Dschang highway and develop other policies in the program for road safety. In addition, this study can as much as possible equally contribute to reorienting road construction trends and development techniques in our environment.

基于Logistic回归模型的TC4零件激光熔化沉积工艺参数分析（英文）

In this paper, laser melting deposition(LMD), a new advanced manufacture technology. While manufacturing a metal part by LMD process, if we could control the energy distribution in internal different areas such as cladding layer or that between cladding layer and the substrate with optimal process parameters, the probability of internal defects of parts can be reduced, and the mechanical properties of parts will be greatly improved. To address the problem that whether the part made by LMD has internal defects, in this paper we designed the orthogonal rotation experiments through selecting different process parameters. Then a Logistic Regression model was built based on the experiments data. The calculation result of the regression model was in good agreement with the result of authentication test. Therefore, this Logistic Regression model has important reference for selecting LMD process parameters.

Alternating Direction Method of Multipliers for l_(1)-l_(2)-Regularized Logistic Regression Model

Logistic regression has been proved as a promising method for machine learning,which focuses on the problem of classification.In this paper,we present anl_(1)-l_(2)-regularized logistic regression model,where thel1-norm is responsible for yielding a sparse logistic regression classifier and thel_(2)-norm for keeping betlter classification accuracy.To solve thel_(1)-l_(2)-regularized logistic regression model,we develop an alternating direction method of multipliers with embedding limitedlBroyden-Fletcher-Goldfarb-Shanno(L-BFGS)method.Furthermore,we implement our model for binary classification problems by using real data examples selected from the University of California,Irvine Machines Learning Repository(UCI Repository).We compare our numerical results with those obtained by the well-known LIBSVM and SVM-Light software.The numerical results show that ourl_(1)-l_(2)-regularized logisltic regression model achieves better classification and less CPU Time.

Analysis of Influential Factors on Agricultural Surplus Labor Professionalization During China's Economic Downturn

This paper contributed to the pool of studies about agricultural surplus labor in China, also acted as the root to the imminent settlement of the issues concerning agriculture, countryside and farmers. Using data from survey of agricultural surplus labor in 2012, which covered three provinces in northern, midwestem and southern parts of China, this paper analyzed the influential factors on agricultural surplus labor professionalization by adoption of a logistic regression model. It showed that agricultural surplus labor shortage could be explained by low-quality professionalization. It was a feasible and effective way to solve the issue of workforce shortage during economic downturn by improving agricultural surplus labor＇s professionalization.

Contraceptive Method Choice Among Newly Married Couples and Influential Factors in Shanghai Municipality

A follow-up study with 7,826 representative newly married couples for fifteen months after their weddings in Shanghai Municipality showed that among the 3, 412 couples who actually adopted contraceptive method, rhythm was the main choice; the proportion for couples taking the contraceptive pill was much higher among sexually active couples before their weddings. The proportions of adopting rhythm or condom or the both, however, increased afterwards.About 86% of couples who had ever planned adopting the rhythm at registration actually used it. In fact, 16% of those who had ever planned to take pills eventually made this choice, because of their worry about any adverse side effects on mother's and fetus' health. Their knowledge about contraception,especially the pills, was incomprehensiue. APProximately 62% of condom users had not been given any instruction regarding its use when they got this contracoptive device one year later. Half of the pill and spermicide users learnt these respective methods from their friends or relatives. The proportion of delivering contraceptiues alter marriage by;F.P.P. was rather low. By fitting the multinomial logistic regression model, it is indicated that couple's evaluation on contraceptiue methods and contraceptiue goal were the main factors determining newlyweds' method of choice. Wife's knowledge on contraception and the accessibility of contraceptives and devices also influenced the method choice to some extent.

Restraint Usage Characteristics and Other Factors Associated with Safety of Children Involved in Motor Vehicle Crashes

Involvement in road traffic crashes as vehicle occupants is a leading cause of death and serious injury among children. The objective of this study was to investigate crash severity factors and child safety restraint use characteristics in order to identify effective countermeasures to increase children＇s highway safety. Characteristics and percentages of restraint use among child passengers aged 4-13 years were examined using highway crash data from Kansas. The association between restraint use, injury severity and characteristics of children involved in crashes were investigated using OR （odds ratios） and a logistic regression model, which was used to identify risk factors. Results showed that children, who were unrestrained, were seated in the front seat, traveling with drunk drivers and on rural roads, and traveling during nighttime was more vulnerable to severe injury in the case of motor vehicle crashes. The most frequent contributing causes related to crashes involving children included driver＇s inattention while driving, failure to yield right-of-way, driving too fast, wet roads and animals in the road. Based on identified critical factors, general countermeasure ideas to improve children＇s traffic safety were suggested, including age-appropriate and size-appropriate seat belt restraints and having children seated in the rear seat. Parents and children must gain better education regarding these safety measures in order to increase child safety on the road.

Improving Disease Prevalence Estimates Using Missing Data Techniques

The prevalence of a disease in a population is defined as the proportion of people who are infected. Selection bias in disease prevalence estimates occurs if non-participation in testing is correlated with disease status. Missing data are commonly encountered in most medical research. Unfortunately, they are often neglected or not properly handled during analytic procedures, and this may substantially bias the results of the study, reduce the study power, and lead to invalid conclusions. The goal of this study is to illustrate how to estimate prevalence in the presence of missing data. We consider a case where the variable of interest (response variable) is binary and some of the observations are missing and assume that all the covariates are fully observed. In most cases, the statistic of interest, when faced with binary data is the prevalence. We develop a two stage approach to improve the prevalence estimates;in the first stage, we use the logistic regression model to predict the missing binary observations and then in the second stage we recalculate the prevalence using the observed data and the imputed missing data. Such a model would be of great interest in research studies involving HIV/AIDS in which people usually refuse to donate blood for testing yet they are willing to provide other covariates. The prevalence estimation method is illustrated using simulated data and applied to HIV/AIDS data from the Kenya AIDS Indicator Survey, 2007.