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

Dengue disease is considered as one of the life threatening disease that has no vaccine to reduce its case fatality. In clinical practice the case fatality of dengue disease can be reduced to 1 if the dengue patients are hospitalized and prompt intravenous fluid therapy is administrated. Yet, it has been a great challenge to the physicians to decide whether to hospitalize the dengue patients or not due to the overlapping of the medical diagnosis criteria of the disease. Beside that physicians cannot decide to admit all patients because this will have major impact on health care cost saving due to the huge incident of dengue disease in the country. Even if the physicians managed to identify the critical cases to be hospitalized, most of the tools that have been used for monitoring those patients are invasive. Therefore, this study was conducted to develop a non-invasive accurate diagnostic system that can assist the physicians to diagnose the risk in dengue patients and therefore attain the correct decision. Bioelectrical Impedance Analysis measurements, Symptoms and Signs presented with dengue patients were incorporated with Adaptive Neuro-Fuzzy Inference System (ANFIS) to construct two diagnostic models. The first model was developed by systematically optimizing the initial ANFIS model parameters while the second model was developed by employing the subtractive clustering algorithm to optimize the initial ANFIS model parameters. The results showed that the ANFIS model based on subtractive clustering technique has superior performance compared with the other model. Overall diagnostic accuracy of the proposed system is 86.13 with 87.5 sensitivity and 86.7 specificity. © 2011 Elsevier Ltd. All rights reserved

The application of biomedical engineering techniques to the diagnosis and management of tropical diseases: A review

This paper reviews a number of biomedical engineering approaches to help aid in the detection and treatment of tropical diseases such as dengue, malaria, cholera, schistosomiasis, lymphatic filariasis, ebola, leprosy, leishmaniasis, and American trypanosomiasis (Chagas). Many different forms of non-invasive approaches such as ultrasound, echocardiography and electrocardiography, bioelectrical impedance, optical detection, simplified and rapid serological tests such as lab-on-chip and micro-/nano-fluidic platforms and medical support systems such as artificial intelligence clinical support systems are discussed. The paper also reviewed the novel clinical diagnosis and management systems using artificial intelligence and bioelectrical impedance techniques for dengue clinical applications

Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

The coronavirus (COVID-19) pandemic has caused severe adverse effects on the human life and the global economy affecting all communities and individuals due to its rapid spreading, increase in the number of affected cases and creating severe health issues and death cases worldwide. Since no particular treatment has been acknowledged so far for this disease, prompt detection of COVID-19 is essential to control and halt its chain. In this paper, we introduce an intelligent fuzzy inference system for the primary diagnosis of COVID-19. The system infers the likelihood level of COVID-19 infection based on the symptoms that appear on the patient. This proposed inference system can assist physicians in identifying the disease and help individuals to perform self-diagnosis on their own cases

Estimation of fines amount in syariah criminal offences using adaptive neuro-fuzzy inference system (ANFIS) enhanced with analytic hierarchy process (AHP)

All syariah criminal cases, especially in khalwat offence have their case-fact, and the judges typically look forward to all the facts which were tabulated by the prosecutors. A variety of criteria is considered by the judge to determine the fines amount that should be imposed on an accused who pleads guilty. In Terengganu, there were ten (10) judges, and the judgments were made by the individual decision upon the trial to decide the case. Each judge has a stake, principles and distinctive criteria in determining fines amount on an accused who pleads guilty and convicted. This research paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS) technique combining with Analytic Hierarchy Process (AHP) for estimating fines amount in Syariah (khalwat) criminal. Datasets were collected under the supervision of registrar and syarie judge in the Department of Syariah Judiciary State Of Terengganu, Malaysia. The results showed that ANFIS+AHP could estimate fines efficiently than the traditional method with a very minimal error

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients

SISTEM INFORMASI PERAMALAN ANGKA KEJADIAN PENYAKIT DEMAM BERDARAH MENGGUNAKAN MULTIVARIATE FUZZY TIME SERIES

Indonesia merupakan negara dengan iklim tropis yang menyebabkan terjadinya dua musim, penghujan dan kemarau. DB atau Demam Berdarah Dengue merupakan penyakit yang biasanya menyerang pada musim penghujan. Namun tidak menutup kemungkinan Demam Berdarah juga menyerang pada musim kemarau. Kabupaten Demak merupakan salah satu daerah di Provinsi Jawa Tengah dengan angka kejadian Demam Berdarah yang cukup rendah dibandingkan dengan kota dan kabupaten lain. Meskipun begitu, pengendalian Demam Berdarah perlu dilakukan untuk meminimalisir terjadinya lonjakan angka kejadian Demam Berdarah, karena Demam Berdarah merupakan penyakit yang cukup berbahaya. Salah satu bentuk pengendalian angka kejadian DB yang banyak digunakan yaitu menggunakan model peramalan, salah satunya yaitu menggunakan Fuzzy Time Series. Model Multivariate Fuzzy Time Series (MFTS) merupakan pengembangan dari model Fuzzy Time Series yang dapat digunakan untuk melakukan peramalan dengan menggunakan data time series dengan menggunakan lebih dari satu variabel untuk peramalan, dibandingkan dengan metode Fuzzy Time Series biasanya hanya menggunakan satu variabel saja. Data aktual yang digunakan untuk peramalan berupa angka kejadian Demam Berdarah, curah hujan dan hari hujan dari bulan Januari 2013 hingga Desember 2018, dengan skenario peramalan 2 tahun training dan testing, 3 tahun training dan testing, 6 tahun training dan testing. Berdasarkan hasil penelitian yang didapat, model MFTS memiliki nilai MAPE yang rata-rata menghasilkan nilai peramalan yang cukup akurat, dengan nilai MAPE terendah, ada pada skenario 3 tahun pada orde 5 dengan MAPE 10,394%. Kata kunci: Demam Berdarah, Multivariate Fuzzy Time Series, Fuzzy Time Series Indonesia is a country with a tropical climate that causes two seasons, the rainy season and the dry season. DHF or Dengue Hemorrhagic Fever is a disease that usually attacks during the rainy season. But it does not rule out DHF also attacking in the dry season. Demak Regency is one of the regions in Central Java Province with a low incidence of Dengue Fever compared to other cities and districts. Even so, DHF control needs to be done to minimize the occurrence of dengue fever, because DHF is a fairly dangerous disease. One form of controlling the number of DHF events that is widely used is using forecasting models, one of which is using Fuzzy Time Series. The Multivariate Fuzzy Time Series (MFTS) model is a development of the Fuzzy Time Series model that can be used to forecast using time series data by using more than one variable for forecasting, compared to the Fuzzy Time Series method usually using only one variable. The actual data used for forecasting are DHF incidence rates, rainfall and rainy days from January 2013 to December 2018, with a forecast scenario of 2 years of training and testing, 3 years of training and testing, 6 years of training and testing. Based on the research results obtained, the MFTS model has an MAPE value that on average produces a fairly accurate forecasting value, with the lowest MAPE value, there is a scenario of 3 years in order 5 with a MAPE of 10.394%. Keywords: Dengue Fever, Multivariate Fuzzy Time Series, Fuzzy Time Serie

Adaptive neuro-fuzzy inference system for predicting alpha band power of EEG during muslim prayer (SALAT)

The features of electroencephalographic (EEG) signals include important information about the function of the brain. One of the most common EEG signal features is alpha wave, which is indicative of relaxation or mental inactivity. Until now, the analysis and the feature extraction procedures of these signals have not been well developed. This study presents a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) for extracting and predicting the alpha power band of EEG signals during Muslim prayer (Salat). Proposed models can acquire information related to the alpha power variations during Salat from other physiological parameters such as heart rate variability (HRV) components, heart rate (HR), and respiration rate (RSP). The models were developed by systematically optimizing the initial ANFIS model parameters. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the optimized ANFIS models. Overall prediction accuracy of the proposed models were achieved of 94.39%, 92.89%, 93.62%, and 94.31% for the alpha power of electrodes positions at O1, O2, P3, and P4, respectively. These models demonstrated many advantages, including e±ciency, accuracy, and simplicity. Thus, ANFIS could be considered as a suitable tool for dealing with complex and nonlinear prediction problems.This research was supported and funded by the Prime Minister's Department, Malaysia (project no. 66-02-03- 0061/H-00000-3703), and University of Malaya, through a postgraduate grant (PS107-2010A)