Basic Findings of Incidence of Breast Cancer in Allied Hospital Faisalabad, Pakistan: A Retrospective Study.

Background: Breast cancer is the most common malignancy among women worldwide. We aimed to know the past trends of age-specific breast cancer incidence rates in Faisalabad city. Methods: A retrospective study was designed at Allied Hospital Faisalabad (AHF), Pakistan from 2014-2018. Overall, 12742 cancer patients presented throughout these years, out of which 3390 were breast cancer cases. Descriptive statistics were computed and the results were presented as counts and percentage for categorical variables. Means and standard errors were computed for the continuous variables. For testing the association among categorical variables, a chi-square test of independence was used and the p-values less than 0.05 are reported as significant. Results: 84.70% patients were diagnosed with invasive breast carcinoma and 15.30% were all other types reported in the Allied Hospital Faisalabad. The incidence of breast cancer was outrageous in the 40-49 year-old age group (1021 patients, 30.12%) and the mean age is 45 in all years. An increase of 34.86% was observed from 2014 to 2018. The comprehensive four-year data (2015 to 2018) were further analyzed for histology, surgery, staging and grading pattern as 2014 files data was insufficient to discuss. The stage III and grade III were most common throughout the years from 2015 to 2018 with 33.9% and 55.71% respectively. Conclusion: Breast cancer is diagnosed more commonly in women than in any other type of cancers in Faisalabad city. There is a need to upgrade the existing hospital facilities to make the women diagnose the cancer at an earlier stage.

Model Selection and Identification of Osteoporosis Risk Factors in Women to Improve Their Healthcare.

Osteoporosis is characterized by low bone mineral density leading to enhanced bone fragility and a consequent increase in fracture risk. The focus of this case-control study was to identify significant socioeconomic risk factors of osteoporosis in Pakistani women and examine how the risk increases for different levels of risk factors. A case-control study was conducted from November 2018 to August 2019 in two main hospitals in Faisalabad, Pakistan. Multiple logistic regression was used to explore the significant risk factors of osteoporosis and how the risk increases in cases (cases = 120) as compared to the control group (controls = 120) in the presence of these risk factors. The mean age ± standard deviation for cases and controls was 59.62 ± 10.75 and 54.27 ± 10.09, respectively. The minimum and maximum ages were 36 and 80 years, respectively. In addition to age, bone fracture, family history, regular physical activity, family size, use of meat, type of birth, breastfeeding, premature menopause, loss of appetite, and use of anticoagulants were significant risk factors with p-values less than 0.05. The risk prediction model with significant risk factors was a good fit with a p-value of 0.28, corresponding to the Hosmer-Lemeshow test value (χ2 = 9.78). This parsimonious model with Cox-Snell R2 = 0.50 (with a maximum value = 0.75) and Nagelkerke R2 = 0.66 showed an AUC of 0.924 as compared to the full model with all risk factors under study that exhibited an AUC of 0.949.

Self-Adaptation Resource Allocation for Continuous Offloading Tasks in Pervasive Computing.

Advancement in technology has led to an increase in data. Consequently, techniques such as deep learning and artificial intelligence which are used in deciphering data are increasingly becoming popular. Further, advancement in technology does increase user expectations on devices, including consumer interfaces such as mobile apps, virtual environments, or popular software systems. As a result, power from the battery is consumed fast as it is used in providing high definition display as well as in charging the sensors of the devices. Low latency requires more power consumption in certain conditions. Cloud computing improves the computational difficulties of smart devices with offloading. By optimizing the device's parameters to make it easier to find optimal decisions for offloading tasks, using a metaheuristic algorithm to transfer the data or offload the task, cloud computing makes it easier. In cloud servers, we offload the tasks and limit their resources by simulating them in a virtual environment. Then we check resource parameters and compare them using metaheuristic algorithms. When comparing the default algorithm FCFS to ACO or PSO, we find that PSO has less battery or makespan time compared to FCFS or ACO. The energy consumption of devices is reduced if their resources are offloaded, so we compare the results of metaheuristic algorithms to find less battery usage or makespan time, resulting in the PSO increasing battery life or making the system more efficient.

Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches.

Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute's SEER Program's November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients' survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K-nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression (accuracy = 80.57 percent) and the greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.

Multiple imputation with compatibility for high-dimensional data.

Multiple Imputation (MI) is always challenging in high dimensional settings. The imputation model with some selected number of predictors can be incompatible with the analysis model leading to inconsistent and biased estimates. Although compatibility in such cases may not be achieved, but one can obtain consistent and unbiased estimates using a semi-compatible imputation model. We propose to relax the lasso penalty for selecting a large set of variables (at most n). The substantive model that also uses some formal variable selection procedure in high-dimensional structures is then expected to be nested in this imputation model. The resulting imputation model will be semi-compatible with high probability. The likelihood estimates can be unstable and can face the convergence issues as the number of variables becomes nearly as large as the sample size. To address these issues, we further propose to use a ridge penalty for obtaining the posterior distribution of the parameters based on the observed data. The proposed technique is compared with the standard MI software and MI techniques available for high-dimensional data in simulation studies and a real life dataset. Our results exhibit the superiority of the proposed approach to the existing MI approaches while addressing the compatibility issue.

Imputation methods for high-dimensional mixed-type datasets by nearest neighbors.

In modern biomedical research, the data often contain a large number of variables of mixed data types (continuous, multi-categorical, or binary) but on some variables observations are missing. Imputation is a common solution when the downstream analyses require a complete data matrix. Several imputation methods are available that work under specific distributional assumptions. We propose an improvement over the popular non-parametric nearest neighbor imputation method which requires no particular assumptions. The proposed method makes practical and effective use of the information on the association among the variables. In particular, we propose a weighted version of the Lq distance for mixed-type data, which uses the information from a subset of important variables only. The performance of the proposed method is investigated using a variety of simulated and real data from different areas of application. The results show that the proposed methods yield smaller imputation error and better performance when compared to other approaches. It is also shown that the proposed imputation method works efficiently even when the number of samples is smaller than the number of variables.

Gender based survival prediction models for heart failure patients: A case study in Pakistan.

OBJECTIVES: The objective of this study was to build and assess the performance of survival prediction models using the gender-specific informative risk factors for patients with left ventricular systolic dysfunction. METHODS: A lasso approach was used to decide the informative predictors for building semi-parametric proportional hazards Cox model. Separate models were built for all patients [N = 299], male patients [Nmale = 194 (64.88%)], and female patients [Nfemale = 105 (35.12%)], to observe the risk factors associated with the individual's risk of death. The likelihood- ratio test was used to test the goodness of fit of the selected model, and the C-index was used to assess the predictive performance of the selected model(s) with respect to the overall model with all observed risk factors. RESULTS: The survival prediction model for females is notably different from that for males. For males, smoking, diabetes, and anaemia, whereas for females, ejection fraction, sodium, and platelets count are non-informative with zero regression coefficients. The goodness of fit of the selected models with respect to the general model with all observed risk factors is tested using the likelihood-ratio test. The results are in favor of the selected models with p-values 0.51,0.61, and 0.70 for all patients, male patients, and female patients, respectively. The same values of C-index for the full model and the selected models for overall data, for males, and for females (0.72, 0.73, and 0.77 for overall data, male data, and female data, respectively) indicate that the selected models are as good as the corresponding overall models regarding their predictive performance. CONCLUSION: There is a substantial difference in the survival prediction models for heart failure (HF) of male and female patients in this study. More studies are needed in Pakistan for confirming this striking male-female difference regarding the potential risk factors to predict survival with heart failure.

Missing value imputation for gene expression data by tailored nearest neighbors.

High dimensional data like gene expression and RNA-sequences often contain missing values. The subsequent analysis and results based on these incomplete data can suffer strongly from the presence of these missing values. Several approaches to imputation of missing values in gene expression data have been developed but the task is difficult due to the high dimensionality (number of genes) of the data. Here an imputation procedure is proposed that uses weighted nearest neighbors. Instead of using nearest neighbors defined by a distance that includes all genes the distance is computed for genes that are apt to contribute to the accuracy of imputed values. The method aims at avoiding the curse of dimensionality, which typically occurs if local methods as nearest neighbors are applied in high dimensional settings. The proposed weighted nearest neighbors algorithm is compared to existing missing value imputation techniques like mean imputation, KNNimpute and the recently proposed imputation by random forests. We use RNA-sequence and microarray data from studies on human cancer to compare the performance of the methods. The results from simulations as well as real studies show that the weighted distance procedure can successfully handle missing values for high dimensional data structures where the number of predictors is larger than the number of samples. The method typically outperforms the considered competitors.