Introduction Increasingly, health care providers are adopting population health management approaches that address the social determinants of health (SDH). However, effectively identifying patients needing services that address a SDH in primary care settings is challenging. The purpose of the current study is to explore how various data sources can identify adult primary care patients that are in need of services that address SDH. Methods A cross-sectional study described patients in need of SDH services offered by a safety-net hospital’s federally qualified health center clinics. SDH services of social work, behavioral health, nutrition counseling, respiratory therapy, financial planning, medical-legal partnership assistance, patient navigation, and pharmacist consultation were offered on a co-located basis and were identified using structured billing and scheduling data, and unstructured electronic health record data. We report the prevalence of the eight different SDH service needs and the patient characteristics associated with service need. Moreover, characteristics of patients with SDH services need documented in structured data sources were compared with those documented by unstructured data sources. Results More than half (53%) of patients needed SDH services. Those in need of such services tended to be female, older, more medically complex, and higher utilizers of services. Structured and unstructured data sources exhibited poor agreement on patient SDH services need. Patients with SDH services need documented by unstructured data tended to be more complex. Discussion The need for SDH services among a safety-net population is high. Identifying patients in need of such services requires multiple data sources with structured and unstructured data.

Using structured and unstructured data to identify patients’ need for services that address the social determinants of health

Introduction: Most Indian women are suffering from Breast Cancer. The simple and efficient screening used for Breast Cancer (BC) is Mammograms. Mammogram images are used to detect BC in the early stages. Objective: The main objective of our research is to detect the BC in early stages using Gray Level Co-occurrence Matrix (GLCM) with Machine Learning Algorithms. Methods: Our proposed system is a two-step process which includes feature extraction and classification. Features are extracted from the Mammographic Image Analysis Society (MIAS) database by using a texture-based descriptor called GLCM. These features are passed to classifiers called K-Nearest Neighbor (KNN), Random Forest (RF) and Gradient Boosting by considering 30% as testing data size. Results: The experiments are done as follows: GLCM+RF, GLCM+KNN and GLCM+ Gradient Boosting and the performance of these classifiers are calculated by finding accuracy metric. Conclusion: The conclusion is that GLCM features with KNN classifier give better results than other classifiers.

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A GLCM based Feature Extraction in Mammogram Images using Machine Learning Algorithms

Classification of mammograms using adaptive binary TLBO with ensemble classifier for early detection of breast cancer

An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection

Modelling Eye-Gaze Movement Using Gaussian Auto-regression Hidden Markov

The commonest disease in Indian women is breast cancer (BC). The Mammography Screening is the best solution to predict breast cancer in early stages. Though more research is going to predict BC in the early stages, the death rate is increasing year by year. So, we proposed a method for efficient classification to identify BC in the early stage. Our system uses efficient shape descriptors and is segmented by using Thresholding. Classification is done by using K-Nearest Neighbor (KNN). Our system classified the mammography images into Normal, Benign or Malignant.

A Novel Approach for Detection of Tumors in Mammographic Images Using Fourier Descriptors and KNN

INTRODUCTION: Breast cancer is the most hazardous disease among women worldwide. A simple, cost-effective, and efficient screening called mammographic imaging is used to find the breast abnormalities to detect breast cancer in the early stages so that the patient’s health can be improved. OBJECTIVES: The main challenge is to extract the features by using a novel technique called Advanced Gray-Level Co-occurrence Matrix (AGLCM) from pre-processed images and to classify the images using machine learning algorithms. METHODS: To achieve this, we proposed a four-step process: image acquisition, pre-processing, feature extraction, and classification. Initially, a pre-processing technique called Contrast Limited Advanced Histogram Equalization (CLAHE) is used to increase the contrast of images and the features are retrieved using AGLCM which extracts texture, intensity and shape-based features as these are important to identify the abnormality. RESULTS: In our framework, a classifier called eXtreme Gradient Boosting (XGBoost) is applied on mammograms and the results are compared with other classifiers such as Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The experiments are done on the Mammographic Image Analysis Society (MIAS) dataset. CONCLUSION: The outcome achieved with CLAHE+ AGLCM+ XGBoost classifier is better than the existing methods. In future, we experiment on large datasets and also concentrate on optimal features selection to increase the classification. Neighbor, Artificial Neural Network, Random Forest and eXtreme Gradient Boosting.

/pdf/a-robust-feature-extraction-technique-for-breast-cancer-18o8clhl.pdf

A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

Breast cancer (BC) is a very prevalent disease among Indian women. The simple and the more effective modality is used for screening the mammograms. The proposed methodology is a 3-step procedure that contains image acquisition, feature extraction, and classification. Our methodology is to retrieve better features from mammograms. These features are classified using a classifier that classifies the images into normal or abnormal. To extract the features from mammograms, an advanced gray-level co-occurrence matrix (AGLCM) technique is used. This extracts texture-, intensity-, and shape-based features. These are fed to several classifiers and achieved better results for artificial neural networks (ANN) classifier. The performance is measured using confusion matrix parameters. The experiments are done on the Mammogram Image Analysis Society (MIAS) dataset. It is observed that AGLCM features with ANN classifier achieve better results compared to other classifiers. The results concluded that our methodology is more effective in classifying mammogram images into normal or abnormal. 

L. Kanya Kumari

Papers

A Novel Approach for Detection of Tumors in Mammographic Images Using Fourier Descriptors and KNN

A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

A Machine Learning Approach for Detection of Breast Cancer in Women Using Advanced GLCM