scholarly journals An Improved Coronary Heart Disease Predictive System Using Random Forest

2021 ◽  
pp. 17-27
Author(s):  
Abdulraheem Abdul ◽  
Rafiu M. Isiaka ◽  
Ronke S. Babatunde ◽  
Jumoke F. Ajao
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Heart Disease ◽  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor

Aims: This work aim is to develop an enhanced predictive system for Coronary Heart Disease (CHD). Study Design: Synthetic Minority Oversampling Technique and Random Forest. Methodology: The Framingham heart disease dataset was used, which was collected from a study in Framingham, Massachusetts, the data was cleaned, normalized, rebalanced. Classifiers such as random forest, artificial neural network, naïve bayes, logistic regression, k-nearest neighbor and support vector machine were used for classification. Results: Random Forest outperformed other classifiers with an accuracy of 98%, a sensitivity of 99% and a precision of 95.8%. Feature selection was employed for better classification, but  no significant improvement was recorded on the performance of the classifier with feature selection. Train test split also performed better that cross validation. Conclusion: Random Forest is recommended for research in Coronary Heart Disease prediction domain.

scholarly journals Phishing Website Detection Using Machine Learning Classifiers Optimized by Feature Selection

2020 ◽  
Vol 37 (4) ◽  
pp. 563-569
Author(s):  
Dželila Mehanović ◽  
Jasmin Kevrić
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Security Threats ◽  
Selection Methods ◽  
K Nearest Neighbor ◽  
Machine Learning Classifiers ◽  
Time To Build ◽  
Fold Cross Validation

Security is one of the most actual topics in the online world. Lists of security threats are constantly updated. One of those threats are phishing websites. In this work, we address the problem of phishing websites classification. Three classifiers were used: K-Nearest Neighbor, Decision Tree and Random Forest with the feature selection methods from Weka. Achieved accuracy was 100% and number of features was decreased to seven. Moreover, when we decreased the number of features, we decreased time to build models too. Time for Random Forest was decreased from the initial 2.88s and 3.05s for percentage split and 10-fold cross validation to 0.02s and 0.16s respectively.

scholarly journals Plant disease prediction using classification algorithms

2021 ◽  
Vol 10 (1) ◽  
pp. 257
Author(s):  
Maria Morgan ◽  
Carla Blank ◽  
Raed Seetan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nearest Neighbor ◽  
Disease Classification ◽  
Future Research ◽  
Support Vector ◽  
Classification Algorithms ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Artificial Neural

<p>This paper investigates the capability of six existing classification algorithms (Artificial Neural Network, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Decision Tree and Random Forest) in classifying and predicting diseases in soybean and mushroom datasets using datasets with numerical or categorical attributes. While many similar studies have been conducted on datasets of images to predict plant diseases, the main objective of this study is to suggest classification methods that can be used for disease classification and prediction in datasets that contain raw measurements instead of images. A fungus and a plant dataset, which had many differences, were chosen so that the findings in this paper could be applied to future research for disease prediction and classification in a variety of datasets which contain raw measurements. A key difference between the two datasets, other than one being a fungus and one being a plant, is that the mushroom dataset is balanced and only contained two classes while the soybean dataset is imbalanced and contained eighteen classes. All six algorithms performed well on the mushroom dataset, while the Artificial Neural Network and k-Nearest Neighbor algorithms performed best on the soybean dataset. The findings of this paper can be applied to future research on disease classification and prediction in a variety of dataset types such as fungi, plants, humans, and animals.</p>

scholarly journals Analisis Perbandingan Algoritma Klasifikasi Citra Chest X-ray Untuk Deteksi Covid-19

Teknika ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 96-103
Author(s):  
Mohammad Farid Naufal ◽  
Selvia Ferdiana Kusuma ◽  
Kevin Christian Tanus ◽  
Raynaldy Valentino Sukiwun ◽  
Joseph Kristiano ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
X Ray ◽  
Chest X Ray

Kondisi pandemi global Covid-19 yang muncul diakhir tahun 2019 telah menjadi permasalahan utama seluruh negara di dunia. Covid-19 merupakan virus yang menyerang organ paru-paru dan dapat mengakibatkan kematian. Pasien Covid-19 banyak yang telah dirawat di rumah sakit sehingga terdapat data citra chest X-ray paru-paru pasien yang terjangkit Covid-19. Saat ini sudah banyak peneltian yang melakukan klasifikasi citra chest X-ray menggunakan Convolutional Neural Network (CNN) untuk membedakan paru-paru sehat, terinfeksi covid-19, dan penyakit paru-paru lainnya, namun belum ada penelitian yang mencoba membandingkan performa algoritma CNN dan machine learning klasik seperti Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) untuk mengetahui gap performa dan waktu eksekusi yang dibutuhkan. Penelitian ini bertujuan untuk membandingkan performa dan waktu eksekusi algoritma klasifikasi K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan CNN  untuk mendeteksi Covid-19 berdasarkan citra chest X-Ray. Berdasarkan hasil pengujian menggunakan 5 Cross Validation, CNN merupakan algoritma yang memiliki rata-rata performa terbaik yaitu akurasi 0,9591, precision 0,9592, recall 0,9591, dan F1 Score 0,959 dengan waktu eksekusi rata-rata sebesar 3102,562 detik.

scholarly journals An Effective Heart Disease Prediction Model based on Machine Learning Techniques

2020 ◽  
Author(s):  
Rony Chowdhury Ripan ◽  
Iqbal H. Sarker ◽  
Md. Hasan Furhad ◽  
Md Musfique Anwar ◽  
Mohammed Moshiul Hoque
Keyword(s):  
Heart Disease ◽  
Prediction Model ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Healthcare Data ◽  
Learning Techniques

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

scholarly journals A hybrid cost-sensitive ensemble for heart disease prediction

2020 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang
Keyword(s):  
Heart Disease ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Misclassification Cost ◽  
Proposed Model ◽  
Learning Machine ◽  
Fold Cross Validation ◽  
Very High

Abstract Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. In this paper, I firstly propose a cost-sensitive ensemble model to improve the accuracy of diagnosis and reduce the misclassification cost. The proposed model contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. Then, experiments are done on three datasets from UCI machine learning repository. The highest classification accuracy of 91.74%, highest G-mean of 90.55%, highest precision of 96.11%, highest recall of 89.61% and lowest misclassification cost of 30.32% are achieved by the proposed model according to ten-fold cross validation. The results demonstrate that the performance of the proposed model is superior to those of previously reported classification techniques.

scholarly journals Studi Komparasi Algoritma Klasifikasi Mental Workload Berdasarkan Sinyal EEG

2020 ◽  
Vol 3 (2) ◽  
pp. 133-143
Author(s):  
Dessy Kusumaningrum ◽  
Elly Matul Imah
Keyword(s):  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Mental Workload ◽  
Principal Component ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
K Nearest Neighbor ◽  
Electroencephalogram Eeg ◽  
Fold Cross Validation

Kondisi psikologis dan fisik manusia dapat memengaruhi proses berpikir. Apabila kondisi individu mengalami kelelahan, maka dapat memengaruhi penurunan tingkat produktivitas maupun penurunan proses berpikir yang menyebabkan timbulnya mental workload. Workload yang dimiliki harus seimbang terhadap kemampuan dan keterbatasan yang dimiliki. Mental workload yang berlebih berdampak buruk bagi individu karena menimbulkan penurunan produktivitas kerja. Perangkat khusus yang dapat digunakan untuk mengetahui tingkat mental workload seorang individu adalah Electroencephalogram (EEG). EEG adalah perangkat khusus yang digunakan untuk mengukur sinyal potensi listrik dari otak. Dataset yang digunakan dalam penelitian ini adalah STEW: Simultaneous Task EEG Dataset dengan 45 subjek. Dalam penelitian ini, telah dilakukan studi komparasi algoritma Random Forest, K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), dan Support Vector Machine (SVM) untuk klasifikasi mental workload berdasarkan sinyal EEG. Studi dilakukan untuk menentukan algoritma terbaik dalam klasifikasi dilihat dari segi nilai akurasi dan penggunaan memori saat proses klasifikasi. Dataset telah melalui beberapa tahapan, diantaranya pra-pemrosesan data, ekstraksi fitur, dan proses klasifikasi. Pra-pemrosesan data menerapkan pembagian data menjadi beberapa chunk. Untuk mendapatkan ciri dalam ekstraksi fitur, diterapkan metode Principal Component Analysis (PCA). Pada proses klasifikasi menggunakan pendekatan k-fold cross validation. Hasil studi penelitian ini adalah algoritma terbaik dari sisi akurasi adalah algoritma KNN, algoritma terbaik dari sisi waktu pembuatan model adalah algoritma Random Forest, serta algoritma terbaik dari sisi penggunaan memori adalah algoritma MLP.

scholarly journals PREDIKSI TINGKAT KESUKSESAN PROMOSI BANK DENGAN ALGORITMA DNN

2021 ◽  
Vol 21 (1) ◽  
pp. 23-33
Author(s):  
Oscar Oscar ◽  
Nurlaelatul Maulidah ◽  
Annida Purnamawati ◽  
Destiana Putri ◽  
Hilman F Pardede
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Success Rate ◽  
Deep Neural Network ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Bank Marketing ◽  
Fold Cross Validation ◽  
Better Than

Telemarketing is one effective way for promoting products. However, it is often difficult to measure the success of telemarketing. Therefore, a way to predict the success rate of telemarketing, and hence strategies could be planned to increase the success rate. In this study, we evaluate several implementations of machine learning for prediction the success of telemarketing. The evaluated methods are Deep Neural Network (DNN), Random Forest, and K-nearest neighbor (K-NN). We validate our experiments using 10-fold cross validation and our experiments show that DNN with 3 hidden layers outperforms other methods. Accuracy of 90% is achieved with the DNN. It is better than Random Forest and KNN that achieve accuracies of algorithm and 88% and 89%.Keywords— Bank Marketing, DNN, KNN, Random Forest.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Quantifying the Influence of Achievement Emotions for Student Learning in MOOCs

2020 ◽  
pp. 073563312096731
Author(s):  
Bowen Liu ◽  
Wanli Xing ◽  
Yifang Zeng ◽  
Yonghe Wu
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Online Courses ◽  
Learning Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Achievement Emotions ◽  
Integrative Framework ◽  
Emotional Interaction ◽  
Performance Results

Massive Open Online Courses (MOOCs) have become a popular tool for worldwide learners. However, a lack of emotional interaction and support is an important reason for learners to abandon their learning and eventually results in poor learning performance. This study applied an integrative framework of achievement emotions to uncover their holistic influence on students’ learning by analyzing more than 400,000 forum posts from 13 MOOCs. Six machine-learning models were first built to automatically identify achievement emotions, including K-Nearest Neighbor, Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machines. Results showed that Random Forest performed the best with a kappa of 0.83 and an ROC_AUC of 0.97. Then, multilevel modeling with the “Stepwise Build-up” strategy was used to quantify the effect of achievement emotions on students’ academic performance. Results showed that different achievement emotions influenced students’ learning differently. These findings allow MOOC platforms and instructors to provide relevant emotional feedback to students automatically or manually, thereby improving their learning in MOOCs.

Sign in / Sign up

Export Citation Format

Share Document