A Fast Fourier Transform-Coupled Machine Learning-Based Ensemble Model for Disease Risk Prediction Using a Real-Life Dataset

Lafta, Raid; Zhang, Ji; Tao, Xiaohui; Li, Yan; Abbas, Wessam; Luo, Yonglong; Chen, Fulong; Tseng, Vincent S.

doi:10.1007/978-3-319-57454-7_51

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…This approach was combined with two methods which are a hybrid method and regression-based prediction algorithm [37] to process the same issue in this field. However, a fast Fourier transformation was used with a machine learning based ensemble model for generating appropriate medical recommendations to patients suffering from chronic diseases [38], [39].…”

Section: Effectiveness Comparison With Previous Methodsmentioning

confidence: 99%

Clustering similar time series data for the prediction the patients with heart disease

Lafta

AL‐Musaylh

Shallal

2022

IJEECS

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Developed intelligent technologies are become play a promising role in providing better decision-making and improving the medical services provided to the patients. A risk prediction task for short-term is big challenge task; however, it is a great importance for recommendation systems in health care field to provide patients with accurate and reliable recommendations. In this work, clustering method and least square support vector machine are used for prediction a short-term disease risk prediction. The clustering similar method is based on euclidean Distance which used to identify the similar sliding windows. The proposed model is trained by using the slide windows samples. Finally, the appropriate recommendations are generated for heart diseases patients who need to take a medical test or not for following day using least square support vector machine. A real dataset which collected from heart diseases patient is used for evaluation. The proposed method yields a good results related by the recommendations accuracy generated to chronicle heart patients and reduce the risk of incorrect recommendations.

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Section: Effectiveness Comparison With Previous Methodsmentioning

confidence: 99%

Clustering similar time series data for the prediction the patients with heart disease

Lafta

AL‐Musaylh

Shallal

2022

IJEECS

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“…Speech is a means of communication between humans, so it is necessary to save voice messages and protect it from any attack (unauthorized persons) [23][24][25]. In this research, speech text messages were encrypted by combining with two of chaotic approaches (Logistic, Sine).…”

Section: Resultsmentioning

confidence: 99%

Speech encryption by multiple chaotic map with fast fourier transform

Alemami

Mohamed

Atiewi

et al. 2020

IJECE

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There are various ways of social communication including writing (WhatsApp, Messenger, Facebook, Twitter, Skype, etc), calling (mobile phone) and voice recording (record your voice and then send it to the other party), but there are ways to eavesdropping the calls and voice messages, One way to solve this problem is via cryptographic approach. Chaos cryptography build on top of nonlinear dynamics chaotic system has gained some footstep in data security. It provides an alternative to conventional cryptography built on top of mathematical structures. This research focuses on the protection of speech recording by encrypting it with multiple encryption algorithms, including chaotic maps (Logistic Map and Sine Maps).

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“…The study found that SVM had an accuracy rate of 80% 18 19. The study Acharya et al [20] Masetic and Subasi [21] Weng et al [22] Lafta et al [23] Frizzell et al [24] compared the performance of Random Forests with other classifiers like Artificial Neural Networks (ANN), C4.5, SVM, Artificial Neural Networks (ANN) and k-Nearest Neighbors (k-NN) and reported that Random Forests shows good accuracy in classifying a subject into normal or CHF when compared to other models. Data consisted of structured records of six patients with a total of 7,147 different time series records.…”

Section: Discussionmentioning

confidence: 99%

“…Neural Network showed an accuracy of 67.5%, sensitivity 67.5% and specificity of 70.7%. Study by Lafta et al [23] tested performance of classifiers ensemble models (FFT-MLE), Neural Networks (NN), Least Square-SVM (LS-SVM) and naive Bayes (NB) in the prediction of heart disease using Tunstall database. Study by Frizzell et al [24] attempted to predict hospital readmission within 30 days in heart failure patients.…”

Section: A Prediction Of Diseases Using Classifiermentioning

confidence: 99%

“…When considering a large unbalance data amongst various features of numerous modalities and different input data types, the multiple kernels are utilized [14]. Several studies have reported their limitations such as performing an evaluation using smaller datasets [12,23,38], not externally validated [34,37,41] interference with results 17. Studies have also reported that complex models are more prone to overfitting and result in poor interpretation [13,25].…”

Section: B Future Recommendationmentioning

confidence: 99%

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Medical Big Data Analytics Using Machine Learning Algorithms

Moorthy,

Gandhi*

2019

IJITEE

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Artificial intelligence and expert systems plays a key role in modern medicine sciences for disease prediction, surveillance interventions, cost efficiency and better quality of life etc. With the arrival of new web-based data sources and systematic data collection through surveys and medical reporting, there is a need of the hour to develop effective recommendation systems which can support practitioners in better decision-making process. Machine Learning Algorithms (MLA) is a powerful tool which enables computers to learn from data. While many novel developed MLA constantly evolves, there is need to develop more systematic, robust algorithm which can interpret with highest possible accuracy, sensitivity and specificity. The study reviews previously published series on different algorithms their advantages and limitations which shall help make future recommendations for researchers and experts seeking to develop an effective algorithm for predicting the likelihood of various diseases.

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A Fast Fourier Transform-Coupled Machine Learning-Based Ensemble Model for Disease Risk Prediction Using a Real-Life Dataset

Cited by 10 publications

References 23 publications

Clustering similar time series data for the prediction the patients with heart disease

Clustering similar time series data for the prediction the patients with heart disease

Speech encryption by multiple chaotic map with fast fourier transform

Medical Big Data Analytics Using Machine Learning Algorithms

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