Prediction Success Rate Research Articles

Prediction of host-pathogen protein interactions by extended network model.

Knowledge of the pathogen-host interactions between the species is essentialin order to develop a solution strategy against infectious diseases. In vitro methods take extended periods of time to detect interactions and provide very few of the possible interaction pairs. Hence, modelling interactions between proteins has necessitated the development of computational methods. The main scope of this paper is integrating the known protein interactions between thehost and pathogen organisms to improve the prediction success rate of unknown pathogen-host interactions. Thus, the truepositive rate of the predictions was expected to increase.In order to perform this study extensively, encoding methods and learning algorithms of several proteins were tested. Along with human as the host organism, two different pathogen organisms were used in the experiments. For each combination of protein-encoding and prediction method, both the original prediction algorithms were tested using only pathogen-host interactions and the same methodwas testedagain after integrating the known protein interactions within each organism. The effect of merging the networks of pathogen-host interactions of different species on the prediction performance of state-of-the-art methods was also observed. Successwas measured in terms of Matthews correlation coefficient, precision, recall, F1 score, and accuracy metrics. Empirical results showed that integrating the host and pathogen interactions yields better performance consistently in almost all experiments.

Landslide susceptibility mapping using state-of-the-art machine learning ensembles

This study propose a new approach through which the landslide susceptibility in Quang Nam (Vietnam) will be estimated using the best model among the following algorithms: Decision Table (DT), Naïve Bayes (NB), Decision Table - Naïve Bayes (DTNB), Bagging Ensemble, Cascade Generalization Ensemble, Dagging Ensemble, Decorate Ensemble, MultiBoost Ensemble, MultiScheme Ensemble, Real Ada Boost Ensemble, Rotation Forest Ensemble, Random Sub Space Ensemble. In this regard, a map with 1130 landslide, was created and further partitioned into training (70%) and testing (30%) locations. The correlation-based features selections (CFS) method was used to select a number of 15 landslide influencing factors. Landslide locations, included in the training sample, and the landslide predictors were used as input data in order to run the above mentioned models. Kappa index, Accuracy (%) and ROC curve were employed to estimate the model’s performance and to test the outcomes provided by the models. Among the eleven machine learning algorithms, Random Sub Space Decision Table Naïve Bayes (RSSDTNB) was the most performant model with an AUC = 0.839, Accuracy = 76.55% and Kappa Index = 0.531. Therefore, this algorithm was involved in the estimation of landslide susceptibility. The Success Rate (AUC = 0.815) and Prediction Rate (AUC = 0.826) revealed the achievement of high-quality results.

English

BACKGROUND This study was conducted to check whether computer tomography (CT) parameter Hounsfield Unit has any bearing on outcome of ureteroscopic pneumatic lithotripsy and as to whether it can predict success rate of ureteroscopic lithotripsy in the management of ureteric calculus. CT Hounsfield Unit tells us about hardness of stone, and it is primarily important in non-invasive management of ureteric and renal calculus such as extracorporeal shock wave lithotripsy (ESWL). METHODS We retrospectively reviewed records of 420 patients who underwent URSL from January 2016 to January 2020. A total of 186 patients of ureteric calculus did undergo CT in pre-operative evaluation for stone. Data of those patients was taken for study. Intra op clearance of calculus was decided by ureteroscopy finding on the table. All complications and difficulties of the procedure were documented. RESULTS We analysed the correlation between the outcome of the URSL and Hounsfield unit in finding the position of the stone and size of the stone. Out of 186 study participants, 111 (59.6 %) patients needed a single procedure for stone clearance whereas 75 (40.4 %) needed multiple procedures for clearance. Out of 186, 22 had HU < 500, 112 had HU 501 - 1000, 52 had HU > 1000. The majority of patients had HU between 501 - 1000HU. Complete clearance was seen in 63.6 % of < 500 HU patients, 62.5 % of 501 - 1000 HU patients and 55.7 % of > 1000 HU patients. This difference in clearance was statistically not significant. Similarly, the rate of complication when compared among the three groups doesn’t show a statistically significant difference (P value 0.293). The requirement of repeat procedure was maximum in > 1000 HU patients but the difference between the three groups was not statistically significant. Stone migration rate was found to be more in > 1000 HU stones (80.76 %) and this was statistically significant. CONCLUSIONS To conclude CT Hounsfield Unit’s utility in predicting the stone-free rate and complication rate doesn’t show significant bearing in patient undergoing URSL procedure. KEYWORDS CT, Hounsfield Unit, URSL, Stone Free Rate, Stone Density, Complication

Modelling habitat suitability of the Indo-Pacific humpback dolphin using artificial neural network: The influence of shipping

The distribution of cetaceans is generally studied on the basis of their visual locations. However, the absence of observations does not exclude the presence of dolphins and not allow to distinguish habitats favourable to the species but where it would be currently absent due to anthropic disturbances. The modelling of ecological niches represents a powerful alternative choice and intensive computer modelling has been increasingly used to reveal the complexity of the relationships between cetaceans and their habitat. Here, we predicted the presence/absence of the Indo-Pacific humpback dolphin (Sousa chinensis), an endangered species, using the artificial neural network model of back-propagation (BP-ANN) with eight environmental variables. The BP-ANN model had a higher success rate for correct prediction (74%) compared to linear discriminant analysis (67%), especially for the prediction of the presence of S. chinensis (63% to 31%), indicating its potential application in cetacean habitat research. In the model output map, three suitable habitats were predicted without S. chinensis sightings identified. However, only one was confirmed by subsequent field surveys, the other two being located in a strong shipping area. Therefore, we suggest that the traditional assessment of the baseline habitat based on visual sighting may miss the identification of some suitable habitats due to anthropogenic disturbance. We have also highlighted the importance of ecological modelling research for cetacean conservation. In addition, among the eight environmental variables studied, distance from shore, fish abundance and salinity proved to be the most important factors for the distribution of S. chinensis, indicating that coastal construction, sea recovery and overfishing would be key constraints for its conservation.

Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

FEASIBILITY AND OUTCOMES OF DOUBLE J STENTING: AN AUDIT IN A HOSPITAL WITHOUT RESIDENT UROLOGIST IN MALAYSIA

Introduction: Ureteric obstruction commonly presents as a urological emergency requiring urgent intervention with urinary diversion. Double J stenting (DJS) is the preferred option to relieve ureteric tract obstruction in our centre. Objective: Our study aims to determine the feasibility and outcomes of DJS by general surgeons and medical officers in a hospital without a resident urologist and to identify the possible predictive factors for successful DJS. Methods: This is a clinical audit of all patients scheduled for DJS in Hospital Bintulu, Sarawak, from January 2017 to March 2020. A total of 57 patients were included. Socio-demographic data and factors predicting success rate, i.e., level of ureteric obstruction and its causes (intrinsic or extrinsic), diagnosis, size and nature of stone(s), degree of hydronephrosis and pre-operative renal profile, were collected. Success rate and postoperative outcomes for all successful cases were evaluated. Bivariate statistical analyses were used to investigate the association of predictive factors with the success rate of DJS. Results: Double J stenting was successful in 63.2% (36 cases) of our total samples of 57 cases. Out of 36 successful cases, 82.1% of the cases were able to return to baseline renal profile within 14 days and 83.3% did not develop fever within 3 days post-operative. The post-operative mean length of stay was 3.16 days and post-operative day 1 mean pain score was 0.72. Causes of obstruction by intrinsic factors, urolithiasis, pre-operative normal serum creatinine (<106 µmol/L) were associated with successful DJS. Conclusion: Double J stenting is feasible and safe to be done in a hospital without a resident urologist with available facilities despite the limitations.

External Validation of the S.T.O.N.E. Score in Predicting Stone-Free Status After Rigid Ureteroscopic Lithotripsy.

BackgroundThe Size, Topography, Obstruction, Number, and Evaluation of Hounsfield units (S.T.O.N.E.) scoring system has been proposed as a novel prognostic surgical classification for urolithiasis in predicting success rate and complications.ObjectiveWe carried out an externally validated S.T.O.N.E. score on rigid ureteroscopic lithotripsy (rURS).Materials and MethodsThe data of patients who had undergone rURS between 2012 and 2019 at a tertiary referral center were audited retrospectively. The S.T.O.N.E. score was calculated based on factors determined through preoperative computed tomography images and was analyzed in association with stone-free rate (SFR), operating time, surgical complications, and length of stay (LOS).ResultsA total of 155 patients were included in the study with a median stone size of 10 mm (7–12) and a median S.T.O.N.E. score of 9 (8–10). The overall SFR was 89.68%. SFRs were 100.0%, 97.83%, and 77.42% in low (5), moderate (6–9), and high (10–13) score groups, respectively. The S.T.O.N.E. score (p = 0.002) and stone size (p = 0.037) were predictive factors for SFR in multivariate analysis. Moreover, there was a significant correlation between the S.T.O.N.E. score and operative time, LOS, and presence of complications (r = 0.22, p = 0.006; r = 0.30, p < 0.001; and r = 0.27, p < 0.001, respectively). The area under the curve of the receiving operator characteristics’ curve for the S.T.O.N.E. score was 0.815.ConclusionThe S.T.O.N.E. scoring system is simple and effective in predicting postoperative outcomes; therefore, this score would be a valuable tool in clinical planning for every patient who undergoes rURS.

A comparative study of the bivariate, multivariate and machine-learning-based statistical models for landslide susceptibility mapping in a seismic-prone region in China

Statistical landslide susceptibility mapping (LSM) models have been most widely used in literatures. However, limitations and uncertainties remain in these methods. The main goal of the current study was to test and compare the efficiency of a bivariate model (the weight of evidence (WoE)), a multivariate model (logistic regression (LR)) and a machine-learning algorithm (the support vector machine (SVM)) in LSM. Lushan County of China was chosen because of its mountainous terrain and high risky of devastating seismic activities. An inventory of 867 landslides was utilized in this study, 70% of which were used to train these models, and the rest 30% were used to validate their accuracies. Ten factors of aspect, elevation, slope, curvature, peak ground acceleration (PGA), distance to the river (DtoR), lithology, topographic wetness index (TWI), stream power index (SPI) and percentage of tree cover (PTC) were used as input of the landslide susceptibility mapping (LSM) models. Accuracy evaluation based on the areas under the receiver operating characteristic curves (AUC) showed that the LR model gives the highest success rate (78.2%) and prediction rate (76.4%), the SVM has the second-highest success rate (75.9%) and the WoE had the second-highest prediction rate (75.6%). Comparison results suggested that the LR and the SVM are proper models for LSM of the study area. The obtained susceptibility maps would benefit regional land planning and seismic landslide hazard mitigation in the study area.

Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications

Precipitable water vapor (PWV) can be assimilated into a numerical weather model (NWM) to improve the prediction accuracy of numerical weather prediction. In this study, taking GNSS data for the Beijing Fangshan station (BJFS) as an example, based on the method of Pearson correlation coefficient combined with quantitative analysis, GNSS datasets are used to study the relationships between GNSS-derived PWV (GNSS PWV_ Met ) and its influencing factors, including the internal influencing factors zenith troposphere delay (ZTD), zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and surface temperature ( T s ), and the external influencing factor haze (mainly PM 2.5 ). Firstly, based on the strong correlation between PWV_ Met and ZTD hourly sequences from the International GNSS Service Network’s BJFS station for DOYS 182–212, 2015, the results of experiment prove that the reliability of GNSS ZTD is used to forecast PWV_Met in short-term forecasting. Secondly, based on hourly data of BJFS in 2016, the correlation between PWV_ Met and ZTD, ZWD, ZHD, pressure (P) and T s is analyzed, and then, with the rate of ZTD variation as the main factor, ZTD variation as auxiliary factor, the prediction success rate is 88.24% from hourly data of precipitation event for DOYs 183–213 in Beijing. The experiment indicates that ZTD can help forecast short-term precipitation. Thirdly, based on data from three hazy periods with relatively stable weather conditions, no heavy rainfall, and relatively continuous data in the past three years, the correlation between GNSS PWV_Met/ZTD and PM 2.5 hourly series is analyzed. The results of the experiments suggests that GNSS ZTD should be considered to assist in haze monitoring. So in the absence of radiosonde stations and meteorological elements, ZTDs on retrieval of GNSS stations have more application value in short-term forecast.

1145 Predicting vaginal birth after cesarean section in black and hispanic patients

Black and Hispanic women have higher rates of cesarean delivery(CD) compared to non-Hispanic white women. One way to decrease the number of CD is to offer vaginal birth after cesarean (VBAC). Prediction models have been used to assist providers in counseling. A widely used calculator(Grobman WA et al.Obstet Gynecol 2007) includes race/ethnicity, which lowers predicted success rates in minority women. Our objective was to examine VBAC success rates in Black and Hispanic women at an urban academic institution. This was a retrospective cohort of Black and Hispanic women with 1 prior CD admitted for delivery from 1/1/17-12/31/18. Patients were grouped according to planned delivery mode(TOLAC or scheduled CD). Exclusion criteria were EGA<24 weeks, fetal chromosome anomalies, IUFD, multiple gestation, uterine rupture at admission and contraindication for TOLAC. Self-identified race/ethnicity was reviewed from the medical record. Two VBAC predicated success rates were calculated; 1. Including race and ethnicity data; 2. Race and ethnicity documented as “no” using this calculator. 266 women met inclusion criteria. 177(66.5%) attempted TOLAC, 138(78%) women had a successful VBAC. 80(45%) were Black and 97(55%) were Hispanic. Age, parity and BMI were similar in those that declined and attempted TOLAC. The mean predicted success rate for patients attempting TOLAC was lower when race/ethnicity data was included(57 vs 73%; p <0.0001). 101(57%) of the women attempting TOLAC had a predicted rate <60%, of which 68(67.3%) had a VBAC. When calculated without race/ethnicity data, 42 were in the <60% group, of which 30(71%) had a VBAC. In women who declined TOLAC, the mean predicted success rate was higher with the second calculation (47.8 vs 62.8%; p<0.0001). Although race/ethnicity decreases predicted success rate using this calculator, our data suggests Black and Hispanic women had high rates of VBAC, even when predicted success rates were low. Removing the social constructs of race/ethnicity from calculation models may improve obstetrical outcomes in minority populations.

Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.

Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated genetic factors must be better understood to search appropriately for anti-aging agents. We utilized an aging-related gene expression pattern-trained machine learning system that can implement reversible changes in aging by linking combinatory drugs. In silico gene expression pattern-based drug repositioning strategies, such as connectivity map, have been developed as a method for unique drug discovery. However, these strategies have limitations such as lists that differ for input and drug-inducing genes or constraints to compare experimental cell lines to target diseases. To address this issue and improve the prediction success rate, we modified the original version of expression profiles with a stepwise-filtered method. We utilized a machine learning system called deep-neural network (DNN). Here we report that combinational drug pairs using differential expressed genes (DEG) had a more enhanced anti-aging effect compared with single independent treatments on leukemia cells. This study shows potential drug combinations to retard the effects of aging with higher efficacy using innovative machine learning techniques.

A deep learning-based CEP rule extraction framework for IoT data

With the recent developments in Internet of Things (IoT), the number of sensors that generate raw data with high velocity, variety, and volume is tremendously increased. By employing complex event processing (CEP) systems, valuable information can be extracted from raw data and used for further applications. CEP is a stream processing technology that matches atomic events to complex events via predefined rules mostly created by experts. However, especially in heterogeneous IoT environments, it may become extremely hard to define these rules manually due to complex and dynamic nature of data. Defining rules requires accurate knowledge of relevant events by utilizing temporal dependencies and relations among attributes of events. Therefore, there is a need for an automatic rule extraction system which is capable of generating rules automatically from unlabeled IoT data. This paper proposes a generalized framework for automatic CEP rule extraction with the help of deep learning (DL) methods. The proposed framework contains two main phases which are data labeling and automatic rule extraction phases. We compare several DL methods with each other and regression-based methods to evaluate the proposed framework. In addition, several rule mining methods are employed in the automatic rule extraction phase in a comparative manner. The reconstruction error and prediction success rate of our framework are evaluated using an air quality dataset collected from a smart city application. The results indicate that the proposed framework is able to generate meaningful and accurate rules for unlabeled IoT data.

Predicting Length of Stay Across Hospital Departments

The length of hospital stay and its implications have a significant economic and human impact. As a consequence, the prediction of that key parameter has been subject to previous research in recent years. Most previous work has analysed length of stay in particular hospital departments within specific study groups, which has resulted in successful prediction rates, but only occasionally reporting predictive patterns. In this work we report a predictive model for length of stay (LOS) together with a study of trends and patterns that support a better understanding on how LOS varies across different hospital departments and specialties. We also analyse in which hospital departments the prediction of LOS from patient data is more insightful. After estimating predictions rates, several patterns were found; those patterns allowed, for instance, to determine how to increase prediction accuracy in women admitted to the emergency room for enteritis problems. Overall, concerning these recognised patterns, the results are up to 21.61% better than the results with baseline machine learning algorithms in terms of error rate calculation, and up to 23.83% in terms of success rate in the number of predicted which is useful to guide the decision on where to focus attention in predicting LOS.

Reliability and Fall-Risk Predictability of the Short Form of the Fullerton Advanced Balance Scale in Iranian Older Adults.

Recently, a short form of the Fullerton Advanced Balance (SF-FAB) scale was reported as a good predictor of falls in older adults. However, we found no evidence regarding its reliability in non-American older adults. Therefore, we aimed to analyze the reliability and homogeneity of the SF-FAB scale to measure postural balance in Iranian older adults. Eighty-five community-dwelling older adults (70.75 ± 4.97years) performed the SF-FAB test on two occasions 1 week apart. In both instances, four raters assessed the performance on the test. The SF-FAB scale (mean total score: 12.46 ± 3.53) revealed acceptable internal consistency (Cronbach's α = .77), excellent intrarater reliability (intraclass correlation coefficient = .94-.99), and excellent interrater reliability (intraclass correlation coefficient = .92-.99). The overall prediction success rate was 83.5% with correctly classifying 95.6% of nonfallers and 35.3% of fallers. The SF-FAB scale can provide a quick screen of balance status in older adults to trigger referral to clinicians for a more comprehensive assessment.

Success Rate of Contemporary Regenerative Endodontic Therapy and the Expected Outcomes of the Endodontic Microsurgery: A Systematic Review and Meta-Analysis

Objective: To examine the level of the accumulating success of the modern Resin-Based Endodontic Surgery (RES) and comparison with Endodontic Microsurgery (EMS) and finally offer a replacement at the predicted final results of EMS. Material and Methods: MEDLINE, PubMed, Cochrane Library, Embase, ISI, Google Scholar have been utilized as electronic databases for systematic literature until 2019. Therefore, Endnote X9, which can be provided in the market, has been applied to manage the electronic titles. Searches have been made with keywords “Endodontic Microsurgery OR EMS ”, “Resin-Based Endodontic Surgery OR RES”, “Regenerative Endodontic Therapy”,” Root-End Filling”, “Root-End Surgery”, “Periapical Surgery” and “Endodontics”. Thus, this systematic review has been conducted concerning the basic investigation of the PRISMA Statement-Preferred Reporting Items designed for the Meta-analyses and Systematic Reviews. Results: A total of RES =19 and EMS =31 with potential pertinent abstracts and topics were discovered in manual and electronic searches. Then, three articles for RES and four studies for EMS publications satisfied our inclusion criteria necessary for systematically reviewing the studies. The analysis showed the success rate for EMS as equal to 1.16 times the probability of the success rate for RES. Conclusion: Micro-surgical procedures superiorly achieved the predictable high success rate for the Root-end surgery compared to conventional methods.

Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network.

This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naive Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.

A Comparative Evaluation of Probabilistic and Deep Learning Approaches for Vehicular Trajectory Prediction

This work compares two innovative methodologies to predict the future locations of moving vehicles when their current and previous locations are known. The two methodologies are based on: (a) a Bayesian network model used to infer the statistics of prior vehicles, trajectory data that is further adopted in the estimation process; (b) a deep learning approach based on recurrent neural networks (RNNs). We present experimental results obtained with both prediction methodologies. The results indicate that the prediction accuracy is improved in both methods as more information about prior vehicle mobility is available. The Bayesian network-based method is advantageous because the statistical inference can be updated in real-time as more trajectory data is known. On the contrary, the RNN-based method requires a time-consuming learning task every time new data is added to the inference dataset. However, the RNN achieves a higher prediction accuracy performance (3% to 5% higher). Additionally, we show that the computational cost to predict the next position a vehicle will move to can be substantially reduced when the Bayesian network is adopted, a scenario where the RNN method requires more computational time. But when the quantity of prior data used in the prediction increases, the computational time required by the RNN-based method can be two orders of magnitude lower, showing that the RNN method is advantageous in both accuracy and computational time. Both methods achieve a next position successful prediction rate higher than 90%, confirming the applicability and validity of the proposed methods.

Prediction of engineering students’ virtual lab understanding and implementation rates using SVM classification

In 2020 many universities were forced to switch to a distant form of education because of the COVID-19 lockdown. This was especially challenging for the engineering specialties, where laboratory and practical exercises are a fundamental part of the educational process. This study presents results from the electrical engineering education in two Bulgarian universities, where the Engine for Virtual Electrical Engineering Equipment was used as a tool for providing virtual labs. At the end of the semester the students were asked to fill in a survey, accounting for their learning program, years of studying, experience with virtual and real labs and the instructions delivery methods used. Data mining algorithms were utilized with the aim to predict students’ rate of understanding and rate of implementation when dealing with virtual labs. Initially, a regression analysis model was created which achieved R-squared above 95%. However, the verification of the model showed an unsatisfactory prediction success rate of 37%. Next, SVM classification was utilized. The verification showed its success rates for predicting the rate of understanding and rate of implementation were 83% and 86%, respectively. This approach could be used to optimize the educational experience of students, using virtual labs, as well as for identification of students that might need additional support and instructions. © Italian e-Learning Association.

Research on Practical Intelligent Mode of Digital Image Economy Based on Improved Genetic Multilayer Neural Network.

In the context of economic globalization and digitization, the current financial field is in an unprecedented complex situation. The methods and means to deal with this complexity are developing towards image intelligence. This paper takes financial prediction as the starting point, selects the artificial neural network in the intelligent algorithm and optimizes the algorithm, forecasts through the improved multilayer neural network, and compares it with the traditional neural network. Through comparison, it is found that the prediction success rate of the improved genetic multilayer neural network increases with the increase of the dimension of the input image data. This shows that, by adding more technical indicators as the input of the combined network, the prediction efficiency of the improved genetic multilayer neural network can be further improved and the advantage of computing speed can be maintained.

Success Rates for the Objectives of US State Cancer Control Plans: A First Look.

BackgroundA well-designed cancer control plan is an important tool for a nation, state, or community to address the burden of cancer. Furthermore, it provides the opportunity to devise and implement measurable objectives. However, there has been little to no assessment of the success rates of such objectives.MethodsI compared the success rate of objectives between US states’ current plan and most recent past plan to determine the proportion of success in the United States overall. I also tested possible reasons for low success rates.ResultsThe mean success rate was 20% for stringent successes (only exact matches between plans) and 28% for loose successes (exact and similar matches between plans). The magnitude of change in percentage between the baseline and target for loose objectives significantly predicted success (P = .0347). Higher change resulted in lower success. However, neither the number of objectives nor the level of overlap significantly predicted success rate. Nor was population size, region, or rural–urban status significantly related to success rate. The most successful states had high proportions of objectives that were measurable and a high number of overlapping objectives.ConclusionI found that objective success rates were low for cancer control plans. To improve success rates, I suggest that future cancer control plans ensure each objective has a measurable baseline and realistically attainable target.