The modern society runs over the social media for their most time of every day. The web users spend their most time in social media and they share many details with their friends. Such information obtained from their chat has been used in several applications. The sentiment analysis is the one which has been applied with Twitter data set toward identifying the emotion of any user and based on those different problems can be solved. Primarily, the data as of the Twitter database is preprocessed. In this step, tokenization, stemming, stop word removal, and number removal are done. The proposed automated learning with CA-SVM based sentiment analysis model reads the Twitter data set. After that they have been processed to extract the features which yield set of terms. Using the terms, the tweets are clustered using TGS-K means clustering which measures Euclidean distance according to different features like semantic sentiment score (SSS), gazetteer and symbolic sentiment support (GSSS), and topical sentiment score (TSS). Further, the method classifies the tweets according to support vector machine (CA-SVM) which classifies the tweet according to the support value which is measured based on the above two measures. The attained results are validated utilizing k-fold cross-validation methodology. Then, the classification is performed by utilizing the Balanced CA-SVM (Deep Learning Modified Neural Network). The results are evaluated and compared with the existing works. The Proposed model achieved 92.48 % accuracy and 92.05% sentiment score contrasted with the existing works.
Several studies aimed at improving healthcare management have shown that the importance of healthcare has grown in recent years. In the healthcare industry, effective decision-making requires multicriteria group decision-making. Simultaneously, big data analytics could be used to help with disease detection and healthcare delivery. Only a few previous studies on large-scale group decision-making (LSDGM) in the big data-driven healthcare Industry 4.0 have focused on this topic. The goal of this work is to improve healthcare management decision-making by developing a new MapReduce-based LSDGM model (MR-LSDGM) for the healthcare Industry 4.0 context. Clustering decision-makers (DM), modelling DM preferences, and classification are the three stages of the MR-LSDGM technique. Furthermore, the DMs are subdivided using a novel biogeography-based optimization (BBO) technique combined with fuzzy C-means (FCM). The subgroup preferences are then modelled using the two-tuple fuzzy linguistic representation (2TFLR) technique. The final classification method also includes a feature extractor based on long short-term memory (LSTM) and a classifier based on an ideal extreme learning machine (ELM). MapReduce is a data management platform used to handle massive amounts of data. A thorough set of experimental analyses is carried out, and the results are analysed using a variety of metrics.
Background and aims COVID-19 has impacted healthcare system worldwide including cancer case. Aim of this study was to describe the experience of lockdown on cancer care concerning patient's visit and reception of treatment in western India. Methods This is a retrospective observational study conducted in patients with cancer attending a tertiary care center pre-lockdown and during lockdown (from January to May 2020). Data related to demographic parameters, type of tumor, type of treatment received and functional status of patients were retrieved from hospital medical records of patients. Results Of the 5258 patients included, 4363 visited hospital pre-lockdown (median age, 50 years) and 895 visited during the lockdown period (median age, 47 years). A total of 1168 and 106 patients visiting hospital before and during lockdown, respectively, had comorbidities. Breast cancer (25.6% and 29.7%), head and neck cancer (21.3% and 16.9%) were the most common type of solid tumors; leukemia (58.0% and 73.0%), lymphoma (18.8% and 13.5%) and multiple myeloma (18.6% and 12.2%) were the most common type of hematological malignancies observed in patients visiting pre-lockdown and during lockdown, respectively. Chemotherapy was most commonly received treatment (pre-lockdown, 71.8%; during lockdown, 45.9%). Other therapies reported includes supportive/palliative, targeted, hormonal, and immunotherapy. The majority of patients who visited the hospital pre-lockdown (68.4%) and during lockdown (62.8%) had 0 or 1 Eastern Cooperative Oncology Group (ECOG) score. Conclusion Overall observations highlight a substantial impact of an imposed nationwide lockdown during COVID-19 pandemic on cancer care of patients in terms of reduced patient visits and number of treatments received.
Cardiovascular Disease or coronary illness is one of the significant dangerous infections in India as well as in the entire world. It is estimated that 28.1 % of deaths occur due to heart diseases. It is also the major cause for significant number of deaths which as more than 17.6 million in the year 2016. So proper and timely diagnosis, treatment of such diseases require a system that can predict with precise accuracy and reliability. Intensive research is carried out by various researchers using diverse machine learning algorithms to forecast the heart disease taking different datasets which consists of different attributes that result in heart attack. In this paper we analyzed the dataset collected from kaggle which consists of attributes related to heart disease such as age, gender, blood pressure, cholesterol and so on. We have also investigated the accuracy levels of various machine learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Decision Trees (DT). The performance and accuracy of above algorithms is not so well when executed using large dataset, so here we tried to improving the prediction accuracy using Artificial Neural Network(ANN), Tensor Flow Keras.
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