Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients

Faisal, Tarig; Taib, Mohd Nasir; Ibrahim, Fatimah

doi:10.1016/j.eswa.2011.09.140

Cited by 52 publications

(26 citation statements)

References 19 publications

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“…Hasil peramalan dengan metode ini bisa digunakan untuk perencanaan penanganan di masa mendatang demam berdarah seperti penyediaan obat, tenaga medis, dan layanan kesehatan lainnya [15].…”

Section: Pendahuluanunclassified

Analisis Kinerja Metode ANFIS untuk Peramalan Kasus Demam Berdarah di Kabupaten Malang

Anggraeni¹,

Kurniaji²,

Riksakomara³

et al. 2018

sisfo

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“…Hasil peramalan dengan metode ini bisa digunakan untuk perencanaan penanganan di masa mendatang demam berdarah seperti penyediaan obat, tenaga medis, dan layanan kesehatan lainnya [15].…”

Section: Pendahuluanunclassified

Analisis Kinerja Metode ANFIS untuk Peramalan Kasus Demam Berdarah di Kabupaten Malang

Anggraeni¹,

Kurniaji²,

Riksakomara³

et al. 2018

sisfo

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“…The numbers 29,14,15,16,17,18,19,20,21,22, and 23 are references for articles containing the respective expert fever diagnosis systems under review for comparison.…”

Section: Comparative Analysis Of Existing and Proposed Expert Fementioning

confidence: 99%

“…Furthermore, the authors of [29], [19] and [21] research work attempted diagnosing multiple fever in their system using artificial intelligence technique. While the authors of [15], [16], [17], [18], [19], [20], [21], [22] and [23] designed a single fever diagnosis system. To further support the importance of artificial intelligence technique in medical diagnosis, the systems developed by authors in [29], [14], [15], [16], [18], [19], [20], [21], [22], and [23] all utilized artificial intelligence technique.…”

Section: Comparative Analysis Of Existing and Proposed Expert Fementioning

confidence: 99%

“…While the authors of [15], [16], [17], [18], [19], [20], [21], [22] and [23] designed a single fever diagnosis system. To further support the importance of artificial intelligence technique in medical diagnosis, the systems developed by authors in [29], [14], [15], [16], [18], [19], [20], [21], [22], and [23] all utilized artificial intelligence technique. Furthermore, we can infer that only our proposed system has multi-fevers symptoms relationship considered, hence making it not just a novel system, but necessary for effective diagnosis of fever as a result of the complication involved in physiological symptoms based diagnosis of fever.…”

Section: Comparative Analysis Of Existing and Proposed Expert Fementioning

confidence: 99%

“…Although the result from this research seem promising , as a computer based typhoid fever diagnosis system with implementation and therapy, but it fails to put into account the symptoms relationship between typhoid and other fever types, which when considered renders the diagnosis rule developed here invalid as some will lead to other fever types. Also, [15] developed a non-invasive cost effective adaptive neuron fuzzy based dengue fever patient risk level diagnosis system, to diagnose dengue fever accurately and determine the risk level of a patient. In order to avoid unwanted hospitalization and its associated cost.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Based Multi-Fever Symptom Classifier Diagnosis Model

Ajenaghughrure¹,

Sujatha²,

Maureen³

2017

IJITCS

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Abstract-Fever has different causes and types, but with similar symptoms. Therefore, making fever diagnosis with human physiological symptoms more complicated. This research project delves into the design of a web based expert multi-fever diagnosis system using a novel fuzzy symptom classifier with human self-observed physiological symptoms. Considering malaria, Lassa, dengue, typhoid and yellow fever. The fuzzy-symptom classifier has two stages. Fist stage is fever type confirmation using common fever symptoms, leading to five major fuzzy rules and the second phase is determining the level of infection (severe or mild) of the confirmed type of fever using unique fever symptoms. Furthermore, Case studies during the system implementation yielded data collected from 50 patients of having different types of fever. The analysis clearly shows the effectiveness and accuracy in the system performance through false result elimination. In addition, acceptability of the system was investigated through structured questionnaire administered to same 50 patients. This result clearly indicates that the system is well accepted, by users and considered fairly easy to use, time and cost saving.Index Terms-Fuzzy classifier, fever diagnosis, multi fever, expert fever diagnosis.

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Prediction of solar radiation in China using different adaptive neuro‐fuzzy methods and M5 model tree

Wang

Kisi

Zounemat‐Kermani

et al. 2016

Intl Journal of Climatology

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Solar radiation is one of the major factors for agricultural, meteorological and ecological applications. In this study, two different optimized adaptive neuro-fuzzy inference systems (ANFIS), ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and M5Tree (M5Tree) methods are proposed for modelling daily global solar radiation (G). Daily meteorological variables at 21 stations in China are used for training and testing the applied models, which is evaluated through root mean square errors (RMSE), mean absolute errors (MAE) and determination coefficient (R 2 ). Above models will be compared with a calibrated empirical Ångström model and the results indicate that the ANFIS models provide better accuracy than the M5Tree and empirical method, for example, the RMSE values for ANFIS-SC, ANFIS-GP, M5Tree and the Ångström model range 2.10-3.08, 2.07-3.08, 2.79-3.87 and 2.54-3.69 MJ m −2 day −1 , respectively. The model performances also show some differences at different stations for each model, for example, the ANFIS models produce the most accurate estimations at station 58238, while M5Tree brings the best accuracy at the station 51777. Meanwhile, the models underestimate high radiation values for some stations, which may due to the differences in training and testing data ranges and distribution of the stations. Finally, the reasons for the differences in model performance are investigated in detail.

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Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients

Cited by 52 publications

References 19 publications

Analisis Kinerja Metode ANFIS untuk Peramalan Kasus Demam Berdarah di Kabupaten Malang

Analisis Kinerja Metode ANFIS untuk Peramalan Kasus Demam Berdarah di Kabupaten Malang

Fuzzy Based Multi-Fever Symptom Classifier Diagnosis Model

Prediction of solar radiation in China using different adaptive neuro‐fuzzy methods and M5 model tree

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