Early Detection Facilities Research Articles

Historical drought and its trend in South Asia: Spatial and temporal analysis 2000-2020

Every year, South Asian countries suffer from declining agricultural outputs due to climate extremes such as floods and droughts. Recurrent droughts have depressed rural economies and enhanced widespread hunger and human migration to South Asian cities (Miyan, 2015). Due to climatic changes, the region is projected to experience rising temperatures and more frequent extreme weather events in the long term (Trenberth et al., 2014). Accurate predictions of drought, its impact, and early detection facilities are not present in most South Asian countries due to a lack of sufficient hydro-meteorological datasets, poor access to satellite products, and shortages of well-trained staff. This study seeks to address these deficiencies scientifically by analysing historical drought conditions on a regional scale using open-access satellite products. The Drought Severity Index (DSI) has been employed to assess meteorological droughts from 2000 to 2020 and to prepare drought severity maps for the South Asian region. Results from DSI were further compared with the Standardised Precipitation Index (SPI) in Nepal and Bangladesh. The results identified pre-monsoon months as the driest period in South Asian countries experiencing severe to moderate drought.

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most commonform of cancer, among women. Breast cancer prediction has been studied by a number of researchers, and is considered as a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models which predicts the outcome in early stages by analyzing history of patient’s data. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR)are used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting(XGBoost). For performance comparison, we employed k-fold cross validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms.

Thermoplasmonic effect onto Toad physiology signals by plasmonic microchip structure

Cardiovascular diseases are considered as the leading cause of death and almost 80% of deaths from this disease are developed in poor and less developed countries where early detection facilities are less available, along with overlooking the importance of screening. In other words, real-time monitoring of the physiological signals using flexible and wearable biosensors plays an important role in human life style. Thus, the present study aims to propose two dimensional flexible and wearable gold covered plasmonic samples as a physiological signal recorder, in which chips with nano array of resonant nanowire patterns performing in an integrated platform of plasmonic devices. The produced surface plasmon waves in our main chip were paired with an electric wave from the heart pulse and it use for recording and detecting the heartbeat of a toad with high accuracy. This measurement was performed in normal state and under external laser heating process to check the ability of signal recording and also thermoplasmonic effect onto the toad's heart signal. Our results show that our sensor was enough sensitive for detection while raising the body temperature of the toad and changing its heart rate as flatting T and P waves by thermoplasmonic effect.

Role of integrative medicine in the continuum of care of breast cancer patients in the Indian context.

Breast cancer is the most frequently diagnosed cancer among women in both transitioned and transitioning countries and has become a major women's health problem. Although recent advances in our understanding of the biological nature of cancer, improved awareness coupled with better early detection facilities, use of chemotherapy, hormone therapy, and targeted therapy have significantly improved survival from cancer, there are many gaps in providing individual-centric, holistic care. Integrative medicine refers to the use of traditional medicine alongside conventional preventive or therapeutic interventions (allopathic medicine) as a comprehensive, individual-centered, evidence-based care. The three pillars of complementary medicine (lifestyle modifications, mind-body practices, and use of natural products) have the potential for cancer prevention and improving quality-of-life and even treatment response in cancer patients when combined with conventional oncology care. Therefore, continued research into integrative therapies is required to extend the benefits to a broader patient population and improve outcomes in breast and other common cancers. In the present review article, the possible role of integrative medicine across the breast cancer care continuum has been discussed along with the concept of integrating complementary practices into mainstream health delivery. We have focused on breast cancer as a model cancer that is well amenable to prevention, early detection and stage appropriate treatment. However, our observations are pertinent for other common cancers, for which there are several opportunities for improving the continuum of care, especially in developing countries like India.

Survival Outcome Prediction for Breast Cancer Patients

The second most causative disease is breast cancer happening in women and a significant explanation behind expanding death rate among women. Observed rates of this cancer are increasing with industrialization and also with early detection facilities. As the finding of this ailment physically takes extended periods and the lesser accessibility of frameworks, there is a need to build up the programmed determination framework for early identification of malignant growth. We have used machine learning classification techniques to categorize benign and malignant tumors, in which the machine learns from past data and predicts the new input category. Models like logistic regression and Random Forest are Done on the UCI dataset. Our experiments have indicated that Random Forest has the best prescient examination with exactness of ~96%.

Prediction of benign and malignant breast cancer using data mining techniques

Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.

Prediction of Benign and Malignant Breast Cancer Using Data Mining Techniques

Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Nai¨ve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases).We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Nai¨ve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.

Deep neck space infections: our experience

<p class="abstract"><strong>Background:</strong> Deep neck space infections (DNSI) are serious diseases that involve several spaces in the neck. The common primary sources of DNSI are tonsil infections, dental infections, salivary gland infections, foreign bodies and malignancies. With widespread use of antibiotics and early detection facilities, the prevalence of DNSI has been reduced. Common complications of DNSI include airway obstruction, jugular vein thrombosis, and sepsis. Treatment principally comprises airway management, antibiotic therapy, and surgical intervention. This study was conducted to investigate the age and sex distribution of patients, symptoms, presentation, sites involved, bacteriology, and management and complications of DNSI.</p><p class="abstract"><strong>Methods:</strong> A prospective study of deep neck space infections was done during the period July 2015 to February 2017 i.e. 20 months who attended the outpatient and were admitted as inpatient in Shadan hospital, Hyderabad.50 Cases were included in the study. All parameters including age, gender, co-morbidities, presentation, site, bacteriology, complications, and investigations were studied. </p><p class="abstract"><strong>Results:</strong> Due to advent of antibiotics, deep neck space infections are in decreasing trend with admission rate accounting to 3% of total inpatients in our hospital. The common age group found to be affected is 10-20 years (28%). Out of all deep neck space infections, Ludwigs infections was common (32%) followed by peritonsillar infections (25%).</p><p><strong>Conclusions:</strong> Infection of the deep neck spaces is a common and potentially life threatening ENT disease and requires an interdisciplinary approach. Tooth infections and foreign body impaction are the commonest causes of these infections. Diabetes Mellitus is an important predisposing factor. Complications such as mediastinitis, septic shock and pleural effusion are possible; hence the surgeon should be cautious. </p>

Modeling Breast Cancer Incidence Rates: A Comparison between the Components of Functional Time Series (FTS) Model Applied on Karachi (Pakistan) and US Data

Several studies showed that the breast cancer incidence rates are higher in high-income (developed) countries, due to the link of breast cancer with several risk factors and the presence of systematic screening policies. Some of the authors suggest that lower breast cancer incidence rates in low-income (developing) countries probably reflect international variation in hormonal factors and accessibility to early detection facilities. Recent studies showed that the breast cancer increased rapidly among women in Pakistan (a developing country) and it became the first malignancy among females of Pakistan. Although, the incidence rates may contain important evidence for understanding and control of the disease; however in Pakistan, the breast cancer incidence data have never been available in the last five decades since independence; rather, only hospital-based data are available. In this study, we intend to apply Functional Time Series (FTS) models to the breast cancer incidence rates of United State (developed country), and to see the difference between various components (age and time) of Functional Time Series (FTS) models applied independently on the breast cancer incidence rates of Karachi (Pakistan) and US. Past studies have already suggested that the incidence of US breast cancer cases was expected to increase in the coming decades. A progressive increase in the number of new cases is already predetermined by the high birth rate that occurred during the middle part of the century, and it will lead to nearly a doubling in the number of cases in about 4 decades. We also obtain 15 years predictions of breast cancer incidence rates in United States and compare them with the forecasts of incidence curves for Karachi. Development of methods for cancer incidence trend forecasting can provide a sound and accurate foundation for planning a comprehensive national strategy for optimal partitioning of research resources between the need for development of new treatments and the need for new research directed toward primary preventive measures.

P2-211 A targeted intervention to promote breast cancer screening among rural women in Coastal Karnataka, India

IntroductionBreast Cancer is the second common cancer among women in India and nearly 21% of the women, who develop the disease, die within the first year of diagnosis. Lack of...

SILENT PHASE OF CANCER OF THE LUNG

Fate has challenged physicians in a peculiar way with respect to primary cancer of the lung. The problem is magnified by an astounding increase in numbers, yet it is simplified by facilities for early detection. In fact, of all internal cancers, those originating in the lung should be the most readily discovered while still locally confined. The lesions not only can be found but can be properly labeled. Excision of the cancer-bearing lung has been shown to be feasible, relatively safe and effective. Therefore physicians have the tools with which to find, label and successfully treat cancer in this location. EXTENT OF PROGRESS Cure of cancer of the lung was never seriously considered prior to 1933, when total excision of the lung was shown to be possible. During subsequent years, correlated studies of symptoms, roentgenographic shadows, bronchoscopic observations, cellular elements of sputum and surgically detected pathologic conditions have been made.