Health Recommender System for Cervical Cancer Prognosis in Women

Kuanr, Madhusree; Mohapatra, Puspanjali; Piri, Jayshree

doi:10.1109/icict50816.2021.9358540

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…An RS used in health applications to analyze patients’ digital data and filter out the best information according to their profile is known as a health recommender system (HRS) [ 61 , 62 ]. HRS can be thought of as a decision-making system that plays a big role in society by advising patients on suitable disease treatments and doctors on good disease diagnoses.…”

Section: Methodsmentioning

confidence: 99%

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

et al. 2022

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Background: Hospitals face a significant problem meeting patients’ medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell–Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings.

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Section: Methodsmentioning

confidence: 99%

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

et al. 2022

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“…The application of a health recommender system to increase the precision and effectiveness of women's cervical cancer prognosis was studied by Kuanr et al [22] in 2021. LR (Logistic Regression), SVC (Support Vector Classifier), DT (Decision Tree), KNN (KNearest Neighbour), GNB (Gaussian Naive Bayes), XGBoost (eXtreme Gradient Boosting), and GBM (Gradient Boosting Machine) are the seven classifiers utilised for the model construction.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Disease Prediction and Drug Recommendation Prototype by Using Multiple Approaches of Machine Learning Algorithms

Nayak,

Garanayak,

Swain

et al. 2023

IEEE Access

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Large blocks of data must be analyzed and explored by utilizing the data mining procedures in order to uncover significant patterns and trends. Medical databases are one area where the data mining procedures can be utilized. Many people all over the world are struggling with their health and medical diagnoses. Massive amounts of data are produced by hospital information systems (HIS), yet it might be difficult to extract knowledge from diagnosis case data. By just giving the symptoms they are experiencing, patients can quickly learn about the sickness they are experiencing and the medication that can assist, treat it using the approaches utilized in this paper. In this paper, we give drug recommendations relied on ratings and conditions to customers. Four distinct prototypes are utilized to predict the diseases. The Vader tool and sentiment analysis relied on NLP are utilized to analyze the reviews. And finally, probabilistic and weighted average methodologies are utilized to recommend the medications. Each model and strategy utilized in this paper is described in detail. The experimental findings presented in this work can be utilized in future studies and for a variety of different medicinal applications.

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“…Other therapeutic areas, such as cervical cancer [59], diabetes [60], and mental health [61] have also been explored. However, they did not implement an intelligent artificial intelligencebased framework [60], and some even lacked a filtering functionality on the recommended activities [61].…”

Section: Related Workmentioning

confidence: 99%

Applying Collective Intelligence in Health Recommender Systems for Smoking Cessation: A Comparison Trial

et al. 2022

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Background: Health recommender systems (HRSs) are intelligent systems that can be used to tailor digital health interventions. We compared two HRSs to assess their impact providing smoking cessation support messages. Methods: Smokers who downloaded a mobile app to support smoking abstinence were randomly assigned to two interventions. They received personalized, ratable motivational messages on the app. The first intervention had a knowledge-based HRS (n = 181): it selected random messages from a subset matching the users’ demographics and smoking habits. The second intervention had a hybrid HRS using collective intelligence (n = 190): it selected messages applying the knowledge-based filter first, and then chose the ones with higher ratings provided by other similar users in the system. Both interventions were compared on: (a) message appreciation, (b) engagement with the system, and (c) one’s own self-reported smoking cessation status, as indicated by the last seven-day point prevalence report in different time intervals during a period of six months. Results: Both interventions had similar message appreciation, number of rated messages, and abstinence results. The knowledge-based HRS achieved a significantly higher number of active days, number of abstinence reports, and better abstinence results. The hybrid algorithm led to more quitting attempts in participants who completed their user profiles.

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Health Recommender System for Cervical Cancer Prognosis in Women

Cited by 8 publications

References 19 publications

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

An Intelligent Disease Prediction and Drug Recommendation Prototype by Using Multiple Approaches of Machine Learning Algorithms

Applying Collective Intelligence in Health Recommender Systems for Smoking Cessation: A Comparison Trial

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