IHEMHA: Interactive healthcare system design with emotion computing and medical history analysis

Ullah, Md. Rifat; Bhuiyan, Md. Ataur Rahman; Das, Amit Kumar

doi:10.1109/iciev.2017.8338606

Cited by 18 publications

(4 citation statements)

References 26 publications

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“…Thus, this score takes into account both false positives and false negatives. Although f1 is less intuitive than accuracy, it is generally more useful if the distribution of classes is uneven [28], [29]. When false positives and false negatives cost the same, accuracy works better.…”

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confidence: 99%

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Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

et al. 2021

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In this present era, sentiment analysis is considered as one of the most rapidly growing fields of computer science study. It is a text mining technique which is automated and determines the emotion of a text. A text can be divided into many emotions using sentiment analysis. Since there are some studies on emotion analysis in the Bangla language, it is regarded as a key research area in the field of analyzing Bangla language. This paper works with five different emotions and those are Happy, Sad, Angry, Surprise and Excited. Apart from these emotions our paper also deals with two categories, such as Abusive and Religious. We proposed a method of machine learning technique which is the SVM algorithm to extract these five individual emotions from Bangla text.

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confidence: 99%

“…The matrix compares actual target values and the ones predicted by the SVM classifier. This gives us a more comprehensive view of our classification model's performance and the errors it is making [28]. Ideally, the confusion matrix would result in values only on the diagonal.…”

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confidence: 99%

Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

et al. 2021

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“…"BDAEH", however, neglects two key variables: a patient's medical history and their genetic information. "IHEMHA", another suggested healthcare system, is concerned with these significant variables and shows the importance of prediction analysis [12].…”

Section: The State-of-artmentioning

confidence: 99%

iHealthcare: Predictive Model Analysis Concerning Big Data Applications for Interactive Healthcare Systems †

2019

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Recently, the healthcare industry has caught the attention of researchers due to a need to develop a smart and interactive system for effective and efficient treatment facilities. The healthcare system consists of massive biological data (unstructured or semi-structured) which needs to be analyzed and processed for early disease detection. In this paper, we have designed a piece of healthcare technology which can deal with a patient’s past and present medical data including symptoms of a disease, emotional data, and genetic data. We have designed a probabilistic data acquisition scheme to analyze the medical data. This model contains a data warehouse with a two-way interaction between high-performance computing and cloud synchronization. Finally, we present a prediction scheme that is performed in the cloud server to predict disease in a patient. To complete this task, we used Random Forest, Support Vector Machine (SVM), C5.0, Naive Bayes, and Artificial Neural Networks for prediction analysis, and made a comparison between these algorithms.

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“…Since the last decade, machine learning has been applied in the medical domain for diagnostic, context-aware healthcare systems, self-management, and treatment and to improve the management of big and complex data to make predictions and prevent risks. Many researchers have studied the capacity of machine learning in the medical domain to predict diseases, define risk thresholds, and develop interactive healthcare systems [12]- [14]. Several context-aware systems focus on the relevant attributes of COPD exacerbations due to the large number of attributes that affect COPD risk factors.…”

Section: Introductionmentioning

confidence: 99%

Discrete separation of patients’ profiles for chronical obstructive pulmonary disease context-aware healthcare efficient systems

Mcheick

Diab²

2023

IJ-AI

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According to the Public Health Agency of Canada (PHAC), the symptoms of chronic obstructive pulmonary disease (COPD) are shortness of breath, coughing, and sputum production. Many studies estimate that COPD will become the third-leading cause of death worldwide by 2030 (WHO,2008). Pervasive healthcare systems cover healthcare issues, including chronic diseases; they help patients to manage their own health information and healthcare services at any time and in any place. We developed a COPD healthcare system based on a combination of the parameters of patients. The main goal is to avoid the severe phases of the disease by monitoring them. This combination of risk factors provides in total 600 profiles from data, with 88.5% accuracy. However, many studies have focused on and shown the issues of the effectiveness and accuracy of these systems. The problem is to i apply a new classification model to detect the severe phases of the disease early. Therefore, Instead of working on COPD parameters, we design and validate a profile-based classification model of patients. This model will facilitate the building of a rule-based framework. In addition, the accuracy of our extended COPD system is improved using the classification and separation of patients’ profiles.

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IHEMHA: Interactive healthcare system design with emotion computing and medical history analysis

Cited by 18 publications

References 26 publications

Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

Sentiment Analysis on Bengali Facebook Comments To Predict Fan's Emotions Towards a Celebrity

iHealthcare: Predictive Model Analysis Concerning Big Data Applications for Interactive Healthcare Systems †

Discrete separation of patients’ profiles for chronical obstructive pulmonary disease context-aware healthcare efficient systems

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