Designing of an expert system based on firefly algorithm for diagnosis of Heart Disease

2016 ◽  
Author(s):  
Naciye Mulayim ◽  
Aysegul Alaybeyoglu
Keyword(s):  
Heart Disease ◽  
Expert System ◽  
Firefly Algorithm

On Sharp Boundary Problem in Rule Based Expert Systems in the Medical Domain

2010 ◽  
Vol 5 (3) ◽  
pp. 14-26
Author(s):  
Oladipupo O. Olufunke ◽  
Uwadia O. Charles ◽  
Ayo K. Charles
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Expert System ◽  
Boundary Problem ◽  
System Approach ◽  
Disease Risk ◽  
Fuzzy Expert System ◽  
Sharp Boundary ◽  
Rule Based

Recently, the application of the conventional rule based expert system for disease risk determination in medical domains has increased. However, a major limitation to the effectiveness of the rule based expert system approach is the sharp boundary problem that leads to underestimation or overestimation of boundary cases, which ultimately affects the accuracy of their recommendation. In this paper, an expert driven approach is used to investigate the viability of a fuzzy expert system in the determination of risk associated with coronary heart disease with regards to the sharp boundary problem in rule based expert system.

scholarly journals Prototype Sistem Pakar untuk Mendeteksi Tingkat Resiko Penyakit Jantung Koroner dengan Metode Dempster-Shafer

2013 ◽  
Vol 7 (2) ◽  
pp. 133 ◽  
Author(s):  
Elyza Gustri Wahyuni ◽  
Widodo Prijodiprodjo
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Expert System ◽  
Expert Systems ◽  
Inference Engine ◽  
Risk Level ◽  
Heart Attacks

AbstrakSistem pakar dapat berfungsi sebagai konsultan yang memberi saran kepada pengguna sekaligus sebagai asisten bagi pakar. Salah satu cara untuk mengatasi dan membantu mendeteksi tingkat resiko penyakit JK seseorang, yaitu dengan membuat sebuah sistem pakar sebagai media konsultasi dan monitoring terhadap seseorang sehingga dapat meminimalkan terjadinya serangan jantung yang mengakibatkan kematian. Metode Dempster-Shafer merupakan metode penalaran non monotonis yang digunakan untuk mencari ketidakkonsistenan akibat adanya penambahan maupun pengurangan fakta baru yang akan merubah aturan yang ada, sehingga metode Dempster-Shafer memungkinkan seseorang aman dalam melakukan pekerjaan seorang pakar. Penelitian ini bertujuan menerapkan metode ketidakpastian Dempster-Shafer pada sistem pakar untuk mendiagnosa tingkat resiko penyakit JK seseorang berdasarkan faktor serta gejala penyakit JK. Manfaat penelitian ini adalah untuk mengetahui keakuratan mesin inferensi Dempster-Shafer.Hasil diagnosa penyakit JK yang dihasilkan oleh sistem pakar sama dengan hasil perhitungan secara manual dengan menggunakan teori mesin inferensi Dempster-Shafer. Sehingga dapat disimpulkan bahwa sistem pakar yang telah dibangun dapat digunakan untuk mendiagnosa PJK. Kata kunci— Dempster-Shafer, Jantung Koroner, Sistem Pakar AbstractThe expert systems can serve as a consultant that  gives advice to the users  and  at once as an assistant to the experts. One way to cope and help detect the risk level of  one’s  coronary heart  disease, is to create the expert system as media of  consulting and monitoring a person so that can minimize the occurrence of heart attacks resulting in death. The Dempster-Shafer method is non monotonis reasoning method is used to look for inconsistencies due to addition or reduction of new facts that will change the existing rules, so that the Dempster-Shafer method enables one safe in doing the expert work. This research aims to apply the Dempster-Shafer uncertainty methods in expert system to diagnose the risk level of one’s coronary heart disease based on factors and symptom of coronary heart disease  The benefits of this research was to know the accuracy of  Dempster-Shafer inference engine.The diagnosis  results of  coronary heart disease  is  generated  by an expert system similarly with manually calculating result using the theory of Dempster-Shafer inference engine. Therefore we can conclude that the expert system that has been built can be used to diagnose Coronary Heart diagnosis. Keywords—Dempster-Shafer, Coronary Heart Disease, Expert Systems

scholarly journals Deep Neural Network for Heart Disease Medical Prescription Expert System

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Majzoob K. Omer ◽  
Osama E. Sheta ◽  
Mohamed S. Adrees ◽  
Deris Stiawan ◽  
Munawar A Riyadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Expert System ◽  
Deep Neural Network ◽  
Medical Prescription

scholarly journals Deep Neural Network for Heart Disease Medical Prescription Expert System

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Majzoob K. Omer ◽  
Osama E. Sheta ◽  
Mohamed S. Adrees ◽  
Deris Stiawan ◽  
Munawar A Riyadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Expert System ◽  
Deep Neural Network ◽  
Medical Prescription

scholarly journals Detection of Heart Abnormalities Based On ECG Signal Characteristics using Multilayer Perceptron with Firefly Algorithm-Simulated Annealing

2021 ◽  
Vol 3 (1) ◽  
pp. 45
Author(s):  
Sofiah Ishlakhul Abda ◽  
Auli Damayanti ◽  
Edi Winarko
Keyword(s):  
Image Processing ◽  
Heart Disease ◽  
Simulated Annealing ◽  
Firefly Algorithm ◽  
Training Data ◽  
Ecg Signal ◽  
Multi Layer Perceptron ◽  
Validation Test ◽  
Signal Characteristics ◽  
Heart Abnormalities

Heart disease is one of the causes of death worldwide. Therefore, detecting heart disease is very important to reduce the increased mortality rate. One of the methods used to detect the abnormalities or disorders of the heart is to use computer assistance to determine the characteristics of an electrocardiogram. Electrocardiogram (ECG) is a test that detects and records the activity of the heart through small metal electrodes attached to the skin of one's chest, arms and legs. This test shows how fast the heart beats and whether the rhythm is stable or not. The purpose of this thesis is to apply a multi-layer perceptron model with firefly algorithm and simulated annealing in detecting cardiac abnormalities based on the ECG signal characteristics. The initial step of this research is image processing. The stages of ECG image processing are grayscale, thresholding, edge detection, segmentation and normalization processes. The results of this image processing are used as input matrices in the perceptron multilayer network training using firefly algorithm and simulated annealing. In the training process, we will get optimal weights and biases for validation tests on test data. The training data in this thesis uses 20 ECG images and in the validation test process uses 10 ECG images. The validation results in the validation test show that the accuracy in detecting heart abnormalities based on the characteristics of ECG signals using multi- layer perceptron with firefly algorithm and simulated annealing is 100%.

Implementasi Metode Penalaran CBR dalam Mengidentifikasi Gejala Awal Penyakit Jantung menggunakan Algoritma Sorensen Coeffient

2021 ◽  
Vol 5 (3) ◽  
pp. 306
Author(s):  
Vicky Agnes Arundy ◽  
Iskandar Fitri ◽  
Eri Mardiani
Keyword(s):  
Heart Rate ◽  
Heart Disease ◽  
Expert System ◽  
Blood Vessels ◽  
The Other ◽  
Case Based Reasoning ◽  
Control Heart ◽  
Left And Right ◽  
The Right ◽  
Case Based

Heart disease is a condition when the heart is experiencing a disorder. The forms of disturbance that are experienced are usually various. Usually there is a disturbance in the blood vessels of the heart, heart rate, heart cover, or congenital problems. The heart itself is a muscle consisting of four chambers. That is, the first two rooms are located at the top, the atrium (foyer) to the left and right. Then the other two rooms are at the bottom, namely the right and left ventricles. To provide information on how to diagnose the type of disease and how to control heart disease, an application of an expert system that can represent someone who is an expert in their field is needed to provide solutions to this disease problem using the Case-Based Reasoning method with the Sorensen Coeffient approach. The result of this research is the creation of an expert system for diagnosing heart disease using the Case-Based Reasoning method with the Sorensen Coeffient approach which is able to provide solutions to heart disease.Keywords:CBR, Expert system, Heart Disease, Method Sorensen Coeffient.

SISTEM PAKAR DIAGNOSA PENYAKIT JANTUNG KORONER DENGAN METODE PROBABILISTIC FUZZY DECISION TREE

2018 ◽  
Vol 4 (2) ◽  
pp. 106
Author(s):  
Wizra Aulia
Keyword(s):  
Blood Pressure ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Chest Pain ◽  
Expert System ◽  
Decision Tree ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Id3 Algorithm ◽  
Anticipatory Action

<p><em>Di Indonesia, penyakit jantung koroner menempati posisi pertama sebagai penyakit yang paling banyak mengakibatkan kematian. Jika gejala penyakit jantung koroner  dikenali sejak dini maka dapat dilakukan tindakan antisipasi. Diagnosa dilakukan berdasarkan 6 gejala penyakit jantung koroner yaitu sakit dada, tekanan darah tinggi, kolesterol, kadar gula darah, hasil EKG dan jumlah denjut jantung. Metode yang dipakai adalah Probabilistic Fuzzy Decision Tree (PFDT) dengan algoritma  Probabilistic Fuzzy  ID3. Hasil keakuratan sistem pakar diagnosa penyakit jantung koroner dengan metode PFDT mencapai 95%.</em><em></em></p><p><em>In Indonesia, coronary heart disease the first position as the disease that most resulted in death. If symptoms of coronary heart disease are recognized early on, anticipatory action may be taken. Diagnosis is based on 6 symptoms of coronary heart disease  chest pain, high blood pressure, cholesterol, blood sugar, ECG results and </em>heartbeat<em>. The method used is Probabilistic Fuzzy Decision Tree (PFDT) with Probabilistic Fuzzy ID3 algorithm. The result of accuracy of expert system of diagnosis of coronary heart disease by PFDT method reached 95%.</em></p>

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