RETRACTED ARTICLE: Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques

Ambika, M.; Raghuraman, G.; SaiRamesh, L.

doi:10.1007/s00500-020-04743-9

Cited by 16 publications

(10 citation statements)

References 35 publications

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“…To demonstrate the superiority of the proposed online prediction scheme, we compared the prediction results with existing batch learning algorithms currently used in medical time series and hypertension risk prediction. We compare the results with Artificial neural networks [19], [15] namely the Long Short Term Memory (LSTM) [49], Baysian based Gaussian regression [50], [51] and Support Vector Machine (SVM) [16]. The data set was divided into training and testing sets.…”

Section: ) Comparison Of Resultsmentioning

confidence: 99%

“…Artificial intelligence is playing a significant role in digitalizing health care, and existing studies extensively discussed hypertension risk prediction. Ambika et al [16] developed a personalised decision support system based on a support vector machine (SVM) and fuzzy association rule mining (ARM) to predict the probability of acquiring hypertension. The missing values in the data are substituted using mean and mode value substitution and the interquartile range (IQR) technique is used to remove the outliers.…”

Section: Related Workmentioning

confidence: 99%

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A Multi-Agent Approach for Personalized Hypertension Risk Prediction

2021

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Hypertension is a global health problem and a leading factor in severe and life-threatening cardiovascular diseases (CVD) and stroke. The onset is dependent on individual lifestyle choices, and no single root cause of the condition exists. Various machine learning solutions are proposed for the early diagnosis of hypertension and its prediction, but they are based on standard guidelines and do not provide personalized solutions. Current models mainly rely on batch learning methods and do not readily learn the new incoming data. There is also a lack of an intelligent technique for handling anomalies in data, which leads to unreliable prediction results. In this paper, an integrated multi-agent-based hypertension risk prediction system is proposed that detects and computes missing values in the time series and provides personalized hypertension risk predictions. The proposed solution incorporates Gaussian mixture models for enhancing the input data, and an Online Infinite Echo State Gaussian Process (OIESGP) is used to obtain real-time prediction distribution of blood pressure. The prediction system readily absorbs new incoming data, and the model is updated to learn any new patterns in the data. The hypertension risk score is estimated using the Framingham hypertension risk estimator, and a 4-year hypertension risk is computed. The prediction performance of the proposed model is evaluated on blood pressure data gathered from the Malaysian population using mean absolute error, mean square error, and root-mean-square errors. The experimental results indicate that the proposed prediction model exhibits greater prediction accuracy than existing state-of-the-art online prediction methods.

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Section: ) Comparison Of Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Multi-Agent Approach for Personalized Hypertension Risk Prediction

2021

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“…A key component of any CDSS is the knowledge base, which uses a traditional way to represent human knowledge. These knowledge-based systems mostly use the rules in the form of IF-THEN statements, and the samples are always according to these patterns of rules ( 29 , 40 , 41 ). Knowledge elicitation is a bottleneck in establishing these systems ( 42 ).…”

Section: Discussionmentioning

confidence: 99%

Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer

Nopour

Shanbehzadeh

Kazemi-Arpanahi³

2021

mjiri

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Background: Colorectal Cancer (CRC) is the most prevalent digestive system-related cancer and has become one of the deadliest diseases worldwide. Given the poor prognosis of CRC, it is of great importance to make a more accurate prediction of this disease. Early CRC detection using computational technologies can significantly improve the overall survival possibility of patients. Hence this study was aimed to develop a fuzzy logic-based clinical decision support system (FL-based CDSS) for the detection of CRC patients.Methods: This study was conducted in 2020 using the data related to CRC and non-CRC patients, which included the 1162 cases in the Masoud internal clinic, Tehran, Iran. The chi-square method was used to determine the most important risk factors in predicting CRC. Furthermore, the C4.5 decision tree was used to extract the rules. Finally, the FL-based CDSS was designed in a MATLAB environment and its performance was evaluated by a confusion matrix.Results: Eleven features were selected as the most important factors. After fuzzification of the qualitative variables and evaluation of the decision support system (DSS) using the confusion matrix, the accuracy, specificity, and sensitivity of the system was yielded 0.96, 0.97, and 0.96, respectively. Conclusion:We concluded that developing the CDSS in this field can provide an earlier diagnosis of CRC, leading to a timely treatment, which could decrease the CRC mortality rate in the community.

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“…Sensors can also be a major source of behaviour prediction data. The work in [17] showcases that passive monitoring using sensors like fall sensors, medical devices and other equipment can be useful when predicting user's behaviour. Sensor data from a sensor carpet is combined with pressure sensor data, motion sensor data, and other medical sensor data like temperature, heart rate, BP and stress levels to form a data vector.…”

Section: Analysis Of Parameters For Human Behavioural Predictionmentioning

confidence: 99%

“…This information can be further researched, and techniques like convolutional neural networks can be used in order to perform complex classification operations that combine diseases from different actions of the users. Such a mechanism is devised in [18], wherein data similar to [17] is processed, but the behavioural patterns used are majorly related to patient's health. The researchers are using higher order classifiers like SVM, and fuzzy logic to perform the task of disease identification.…”

Section: Analysis Of Parameters For Human Behavioural Predictionmentioning

confidence: 99%

Human Behavior Prediction and Analysis Using Machine Learning-A Review

Gulhane¹

2021

TURCOMAT

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Nowadays many trends are being in the area of medicine to predict the human behaviour and analysis of patient behaviour is being studied but the technical difficulty of cost efficient method to predict the behaviour of user is overcome in the proposed researched methodology .The mental health of the used can lead to good immunity system to be healthy in this pandemic of COVID-19. Hence After a detailed study on different human health disease classification techniques it is found that machine learning techniques are reliable for the feature extraction and analysis of the different human parameters. CNN is the most optimum choice of classification of diseases. Feature extraction and feature selection is automatically managed by the CNN layers, which reduces the training speed. Techniques like sensor-based feature extraction like EEG, ECG, etc. will be further explored using machine learning algorithms for detection of early detections of diseases from human behavior on different platforms in this research. Social behavior and eating habits play a vital role in disease detection. A system that combines such a wide variety of features with effective classification techniques at each stage is needed. The research in this paper contributes the review of the human behavior analysis through different body parameters, food habits and social media influences with social behavior of the person. The main objective of research is to analysis theses different area parameters to predict the early signs of the diseases.

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RETRACTED ARTICLE: Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques

Cited by 16 publications

References 35 publications

A Multi-Agent Approach for Personalized Hypertension Risk Prediction

A Multi-Agent Approach for Personalized Hypertension Risk Prediction

Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer

Human Behavior Prediction and Analysis Using Machine Learning-A Review

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