scholarly journals Heart Disease Prediction using Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 1907-1910
Author(s):  
Ganesh Nanekar
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Whole Body ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Monthly Basis ◽  
Related Data ◽  
Learning Techniques

Heart is the next major organ comparing to brain which has more priority in Human body. It pumps the blood and supplies to all organs of the whole body. Prediction of occurrences of heart diseases in medical field is significant work. Data analytics is useful for prediction from more information and it helps medical Centre to predict of various disease. Huge amount of patient related data is maintained on monthly basis. The stored data can be useful for source of predicting the occurrence of future disease. Some of the data mining and machine learning techniques are used to predict the heart disease, such as Decision tree, Fuzzy Logic, K-Nearest Neighbor (KNN), Naïve Bayes and Support Vector Machine (SVM). This paper provides an insight of the existing algorithms and implements hybrid algorithms to improve accuracy significantly.

scholarly journals Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 267-276
Author(s):  
Baban. U. Rindhe ◽  
Nikita Ahire ◽  
Rupali Patil ◽  
Shweta Gagare ◽  
Manisha Darade
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Whole Body ◽  
Support Vector ◽  
Learning Techniques

Heart-related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need fora reliable, accurate, and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart-related diseases. Heart is the next major organ comparing to the brain which has more priority in the Human body. It pumps the blood and supplies it to all organs of the whole body. Prediction of occurrences of heart diseases in the medical field is significant work. Data analytics is useful for prediction from more information and it helps the medical center to predict various diseases. A huge amount of patient-related data is maintained on monthly basis. The stored data can be useful for the source of predicting the occurrence of future diseases. Some of the data mining and machine learning techniques are used to predict heart diseases, such as Artificial Neural Network (ANN), Random Forest,and Support Vector Machine (SVM).Prediction and diagnosingof heart disease become a challenging factor faced by doctors and hospitals both in India and abroad. To reduce the large scale of deaths from heart diseases, a quick and efficient detection technique is to be discovered. Data mining techniques and machine learning algorithms play a very important role in this area. The researchers accelerating their research works to develop software with thehelp of machine learning algorithms which can help doctors to decide both prediction and diagnosing of heart disease. The main objective of this research project is to predict the heart disease of a patient using machine learning algorithms.

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 Comparative Study on University Admission Predictions Using Machine Learning Techniques

2021 ◽  
pp. 537-548
Author(s):  
Prince Golden ◽  
Kasturi Mojesh ◽  
Lakshmi Madhavi Devarapalli ◽  
Pabbidi Naga Suba Reddy ◽  
Srigiri Rajesh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Education Systems ◽  
University Admissions ◽  
The Troubles ◽  
Learning Techniques ◽  
Cloud Servers

In this era of Cloud Computing and Machine Learning where every kind of work is getting automated through machine learning techniques running off of cloud servers to complete them more efficiently and quickly, what needs to be addressed is how we are changing our education systems and minimizing the troubles related to our education systems with all the advancements in technology. One of the the prominent issues in front of students has always been their graduate admissions and the colleges they should apply to. It has always been difficult to decide as to which university or college should they apply according to their marks obtained during their undergrad as not only it’s a tedious and time consuming thing to apply for number of universities at a single time but also expensive. Thus many machine learning solutions have emerged in the recent years to tackle this problem and provide various predictions, estimations and consultancies so that students can easily make their decisions about applying to the universities with higher chances of admission. In this paper, we review the machine learning techniques which are prevalent and provide accurate predictions regarding university admissions. We compare different regression models and machine learning methodologies such as, Random Forest, Linear Regression, Stacked Ensemble Learning, Support Vector Regression, Decision Trees, KNN(K-Nearest Neighbor) etc, used by other authors in their works and try to reach on a conclusion as to which technique will provide better accuracy.

scholarly journals The Role of Medication Data to Enhance the Prediction of Alzheimer’s Progression Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Bader Alouffi ◽  
Radhya Sahal ◽  
Naglaa Abdelhade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Multimodal Data ◽  
Learning Techniques ◽  
Early Progression ◽  
Neuroimaging Data

Early detection of Alzheimer’s disease (AD) progression is crucial for proper disease management. Most studies concentrate on neuroimaging data analysis of baseline visits only. They ignore the fact that AD is a chronic disease and patient’s data are naturally longitudinal. In addition, there are no studies that examine the effect of dementia medicines on the behavior of the disease. In this paper, we propose a machine learning-based architecture for early progression detection of AD based on multimodal data of AD drugs and cognitive scores data. We compare the performance of five popular machine learning techniques including support vector machine, random forest, logistic regression, decision tree, and K-nearest neighbor to predict AD progression after 2.5 years. Extensive experiments are performed using an ADNI dataset of 1036 subjects. The cross-validation performance of most algorithms has been improved by fusing the drugs and cognitive scores data. The results indicate the important role of patient’s taken drugs on the progression of AD disease.

Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms

2020 ◽  
pp. 1577-1597
Author(s):  
Kusuma Mohanchandra ◽  
Snehanshu Saha
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Support Vector ◽  
Test Bed ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Broad Perspective ◽  
Learning Techniques

Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

On the Use of Artificial Intelligence Techniques in Crop Monitoring and Disease Identification

2021 ◽  
pp. 166-186
Author(s):  
Muzaffer Kanaan ◽  
Rüştü Akay ◽  
Canset Koçer Baykara
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Crop Yields ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Disease Identification ◽  
Use Of Technology ◽  
Learning Techniques

The use of technology for the purpose of improving crop yields, quality and quantity of the harvest, as well as maintaining the quality of the crop against adverse environmental elements (such as rodent or insect infestation, as well as microbial disease agents) is becoming more critical for farming practice worldwide. One of the technology areas that is proving to be most promising in this area is artificial intelligence, or more specifically, machine learning techniques. This chapter aims to give the reader an overview of how machine learning techniques can help solve the problem of monitoring crop quality and disease identification. The fundamental principles are illustrated through two different case studies, one involving the use of artificial neural networks for harvested grain condition monitoring and the other concerning crop disease identification using support vector machines and k-nearest neighbor algorithm.

Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms

2017 ◽  
pp. 186-206
Author(s):  
Kusuma Mohanchandra ◽  
Snehanshu Saha
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Support Vector ◽  
Test Bed ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Broad Perspective ◽  
Learning Techniques

Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

Human Computer Interface Using Electroencephalography for Driver Behavior Classification

2019 ◽  
Author(s):  
Vamsi K. Manchala ◽  
Alvaro V. Clara ◽  
Susheelkumar C. Subramanian ◽  
Sangram Redkar ◽  
Thomas Sugar
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Driver Behavior ◽  
Driving Behavior ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Affective States ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Artificial Neural Network Ann

Abstract It is important to know and be able to classify the drivers’ behavior as good, bad, keen or aggressive, which would aid in driver assist systems to avoid vehicle crashes. This research attempts to develop, test, and compare the performance of machine learning methods for classifying human driving behavior. It also proposes to correlate driver affective states with the driving behavior. The major contributions of this work are to classify the driver behavior using Electroencephalograph (EEG) while driving simulated vehicle and compare them with the behavior classified using vehicle parameters and affective states. The study involved both classical machine learning techniques such as k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and latest “unsupervised” Hybrid Deep Learning techniques, and compared the accuracy of classification across subjects, various driving scenarios and affective states.

scholarly journals Coronary Illness Prediction Using Random Forest Classifier

2021 ◽  
Author(s):  
Rekha G ◽  
Shanthini B ◽  
Ranjith Kumar V
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Heart Diseases ◽  
Surrogate Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Techniques

Heart diseases or Cardiovascular Diseases (CVDs) are the main cause of death on the planet throughout the most recent years and become the most dangerous disease in India and the entire world. The UCI repository is utilized to calculate the exactness of the AI calculations for foreseeing coronary illness, as k-nearest neighbor, decision tree, linear regression, and support vector machine. Different indications like chest pain, fasting of heartbeat, etc., are referenced. Large datasets, which are not available in medical and clinical research, are required in order to apply deep learning techniques. Surrogate data is generated from Cleveland dataset. The predicted results show that there is an improvement in classification accuracy. Heart disease is one of the most challenging diseases to diagnose as it is the most recognized killer in the present day. Utilizing AI algorithms, this paper gives anticipating coronary illness. Here, we will use the various machine learning algorithms such as Support Vector Machine, Random Forest, KNN, Naive Bayes, Decision Tree and LR.

scholarly journals Treatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques

2021 ◽  
Vol 1 (2) ◽  
pp. 79-89
Author(s):  
Ashfaq Ali Kashif ◽  
Birra Bakhtawar ◽  
Asma Akhtar ◽  
Samia Akhtar ◽  
Nauman Aziz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatitis C ◽  
Treatment Response ◽  
Nearest Neighbor ◽  
Response Prediction ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Treatment Response Prediction

The proper prognosis of treatment response is crucial in any medical therapy to reduce the effects of the disease and of the medication as well. The mortality rate due to hepatitis c virus (HCV) is high in Pakistan as well as all over the world. During the treatment of any disease, prediction of treatment response against any particular medicine is difficult. This paper focuses on predicting the treatment response of a drug: “L-ornithine L-Aspartate (LOLA)” in hepatitis c patients. We have used various machine learning techniques for the prediction of treatment response, including: “K Nearest Neighbor, kStar, Naive Bayes, Random Forest, Radial Basis Function, PART, Decision Tree, OneR, Support Vector Machine and Multi-Layer Perceptron”. Performance measures used to analyze the performance of used machine learning techniques include, “Accuracy, Recall, Precision, and F-Measure”.

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