Particle Swarm Optimisation-Support Vector Machine Optimised by Association Rules for Detecting Factors Inducing Heart Diseases

2017 ◽  
Vol 26 (3) ◽  
pp. 573-583
Author(s):  
Lu Wei-Jia ◽  
Ma Liang ◽  
Chen Hao
Keyword(s):  
Support Vector Machine ◽  
Association Rules ◽  
Heart Diseases ◽  
Particle Swarm ◽  
Detection Algorithm ◽  
Classification Performance ◽  
Particle Swarm Optimisation ◽  
Support Vector ◽  
Classification Algorithms ◽  
Data Set

AbstractExisting systems for diagnosing heart diseases are time consuming, expensive, and error prone. Aiming at this, a detection algorithm for factors inducing heart diseases based on a particle swarm optimisation-support vector machine (PSO-SVM) optimised by association rules (ARs) was proposed. Firstly, AR was used to select features from a disease data set so as to train feature sets. Then, PSO-SVM was used to classify training and testing sets, and then the factors inducing heart diseases were analysed. Finally, the effectiveness and reliability of the proposed algorithm was verified by experiments on the UCI Cleveland data set with confidence as the index. The experimental results showed that females have less risk of having a heart attack than males. Irrespective of gender, once diagnosed with chest pain without symptoms and angina caused by exercise, people are more likely to suffer from heart disease. Moreover, compared with another two advanced classification algorithms, the proposed algorithm showed better classification performance and therefore can be used as a powerful tool to help doctors diagnose and treat heart diseases.

scholarly journals Diabetes Prediction Using Machine Learning Techniques

2021 ◽  
pp. 150-152
Author(s):  
Seyma Kiziltas Koc ◽  
Mustafa Yeniad
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
High Performance ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Machine Learning Classification

Technologies which are used in the healthcare industry are changing rapidly because the technology is evolving to improve people's lifestyles constantly. For instance, different technological devices are used for the diagnosis and treatment of diseases. It has been revealed that diagnosis of disease can be made by computer systems with developing technology.Machine learning algorithms are frequently used tools because of their high performance in the field of health as well as many field. The aim of this study is to investigate different machine learning classification algorithms that can be used in the diagnosis of diabetes and to make comparative analyzes according to the metrics in the literature. In the study, seven classification algorithms were used in the literature. These algorithms are Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron, Random Forest, Decision Trees, Support Vector Machine and Naive Bayes. Firstly, classification performance of algorithms are compared. These comparisons are based on accuracy, sensitivity, precision, and F1-score. The results obtained showed that support vector machine algorithm had the highest accuracy with 78.65%.

scholarly journals SENTIMEN ANALISIS OPERASI TANGKAP TANGAN KPK MENURUT MASYARAKAT MENGGUNAKAN ALGORITMA SUPPORT VECHTOR MACHINE, NAÏVE BAYES, BERBASIS PARTICLE SWARM OPTIMIZITION

Faktor Exacta ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 230
Author(s):  
Hernawati Hernawati ◽  
Windu Gata Kedua
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
Bayes Method ◽  
Machine Method ◽  
Swarm Optimization ◽  
Support Vector Machine Method

<p><em>It is known from various public sentiments conveyed through comments on social media twitter against the capture operations carried out by the corruption eradication commission (KPK) that currently it does not meet the expectations of the community, where officials who are only officials have small corruption rates, not corruption As for the classification algorithms that have strong accuracy at this time are Support Vector Machine and Naïve Bayes algorithms, calculation of Support Vector Machine method for tweet data from 78 positive tweet data and 78 negative tweet data, resulting in an accuracy of 80.77% and AUC 0.867. Whereas the results of accuracy with the Naïve Bayes method are 76.92% and AUC 0.729. Having a difference in accuracy of 3.3%, and after optimizing with the Operator Vector Machine (PSO) weight Particle Swarm Optimization the accuracy is 83.79% and AUC 0.910, while for Naïve Bayes (PSO) produces an accuracy of 80.13% and AUC 0.771 Has a difference in accuracy of 3.6%.</em></p><p><em><br /></em></p><p><em>Diketahui dari berbagai sentimen masyarakat yang disampaikan melalui komentar di media sosial <em>twiter </em>terhadap operasi tangkap tangan yang dilakukan oleh Komisi Pemberantasan Korupsi (KPK) nyatanya saat ini belum memenuhi harapan masyarakat, dimana pejabat yang di ott hanya pejabat yang mempunyai angka korupsi kecil, bukan korupsi yang besar adapun algoritma klasifikasi yang kuat akurasinya saat ini adalah algoritma<em> Support Vector Machine </em>untuk data <em>tweet</em> dari 78 data tweet positif dan 78 data tweet negatif, menghasilkan akurasi sebesar 80.77% dan AUC 0.867. Sedangkan hasil akurasi dengan metode<em> Naïve Bayes</em> adalah 76.92% dan AUC 0.729. Memiliki selisih akurasi sebesar 3.3%, dan setelah di optimalisasi dengan oprator <em>Weight Partical Swarm Optimization</em> untuk <em>Support Vector Machine (PSO)</em> menghasilkan akurasi 83.79% dan AUC 0.910, sedangkan untuk <em>Naïve Bayes</em> (PSO) menghasilkan akurasi sebesar 80.13% dan AUC 0.771 memiliki selisih akurasi sebesar 3.6%.</em></p>

PSO-SVM-based gait phase classification during human walking on unstructured terrains: Application in lower-limb exoskeleton

2019 ◽  
Vol 233 (19-20) ◽  
pp. 7144-7154
Author(s):  
Jiajia Zheng ◽  
Jianhua Chen ◽  
Mingxing Yang ◽  
Song Chen
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
Particle Swarm ◽  
Support Vector ◽  
Data Set ◽  
Swarm Optimization ◽  
Phase Classification ◽  
Gait Phase

Gait analysis is of great importance to ensure that gait phases induced by robotic exoskeleton are tailored to each individual and external complex environments. The objective of this work is to develop a pressure insole system with redundant functionality for gait phase classification based on the analysis of ground reaction force on unstructured terrains. A support vector machine optimized by particle swarm optimization was proposed for classifying four gait phases including initial contact, mid stance, terminal stance and swing phase. Seven pressure sensors are employed according to the plantar distribution contour of ground reaction force and walking data acquisition is conducted on treadmill, concrete pavement and wild grassland, respectively. Two classifiers, support vector machine-based classifier I and PSO-SVM-based classifier II are constructed on the basis of gait data set obtained on treadmill. Experimental results showed that the proposed PSO-SVM algorithm exhibits distinctive advantages on gait phase classification and improves the classification accuracy up to 32.9%–42.8%, compared with that of classifier based solely on support vector machine. In addition, some unwanted errors, intentional attacks or failures can be successfully solved with fast convergence rate and good robustness by using particle swarm optimization.

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