Prediction of gastric ulcers based on the change in electrical resistance of acupuncture points using fuzzy logic decision-making

Al-Kasasbeh, Riad Taha; Korenevskiy, Nikolay; Alshamasin, Mahdi Salman; Ionescou, Florin; Smith, Andrew P.

doi:10.1080/10255842.2011.618926

Cited by 38 publications

(7 citation statements)

References 11 publications

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“…Thus far, many previous studies have been focused on the application of fuzzy modeling in early GI malignancy prognosis, risk assessment, and survivability prediction ( 14 , 22 , 23 , 49 ). Some of them demonstrated that applying fuzzy logic for CRC risk assessment can reduce diagnosis errors and disagreement among physicians at any level of prediction, prevention, and treatment.…”

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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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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“…Last but not least, a publication from Al-Kasasbeh et al [11] should be mentioned here. The analysis of mathematical models of the interaction of the internal and BAPs of meridian structures allows the specification of a list of gastric diseases for which reflex diagnostics and reflex therapy methods are most effective [11]. …”

Section: Discussionmentioning

confidence: 99%

“…Another example shows that good results for the prediction and early diagnosis of diseases from the reaction energy of BAPs are obtained using complicated fuzzy logic decision making systems [10]. Last but not least, a publication from Al-Kasasbeh et al [11] should be mentioned here. The analysis of mathematical models of the interaction of the internal and BAPs of meridian structures allows the specification of a list of gastric diseases for which reflex diagnostics and reflex therapy methods are most effective [11].…”

Section: Discussionmentioning

confidence: 99%

Patterns of Skin Luminescence Resulting from the Visualization of Active Acupuncture Points Using Optical Stimulation

Navrotsky¹,

Блохин²,

Belavskaya³

et al. 2015

Integr Med Int

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Background: In recent years, biologically active points (BAPs) have become the topic of intensive scientific discussion. Innovative investigations of this topic are presented. Methods: Types of skin surface luminescence patterns in the areas of BAPs observed after acting on the area with a short light pulse are analyzed. The device was designed as a laboratory model (NSTU, medical electronics laboratory, Novosibirsk, Russia). Pictures were taken with a PENTAX K10 , a PENTAX ist DL as well as a Canon 6D photo camera. Luminescent points can clearly be observed. The pictures were zoomed on a computer and graphically analyzed. Results: Five patterns were found: black center with white ring, black center with 2 rings (white and black), white center with black ring, white center with 2 rings (black and white) and white center with 3 rings (black, white and black). Luminescent BAPs were found on the pericardium and triple energizer meridians. Conclusion: This pilot study can serve as a basis for further investigations in the field of meridian research. The first results are very promising, and future studies are indicated in body acupuncture as well as ear acupuncture.

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“…Four studies using fuzzy logic were excluded. We determined that fuzzy logic is a technique that converts a linguistic variable, not a deep learning technique in itself (Kharal, 2009;Al-Kasasbeh et al, 2011;Al-Kasasbeh et al, 2012;Al-Kasasbeh et al, 2013). Studies on quality evaluation of herbs or traditional medicines were excluded because it was difficult to consider the use of CAM modalities (Tian et al, 2012;Wang et al, 2012;Tan et al, 2020).…”

Section: Study Selection Processmentioning

confidence: 99%

The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review

Chu¹,

Moon

Park

et al. 2022

Front. Pharmacol.

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Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status.Objective: To categorize and seek the current usage of AI in CAM.Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies.Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians.Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.

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Prediction of gastric ulcers based on the change in electrical resistance of acupuncture points using fuzzy logic decision-making

Cited by 38 publications

References 11 publications

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

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

Patterns of Skin Luminescence Resulting from the Visualization of Active Acupuncture Points Using Optical Stimulation

The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review

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