scholarly journals HEART DISEASE PREDICTION USING MACHINE LEARNING TECHNIQUES

2021 ◽  
Vol 9 (1) ◽  
pp. 207-214
Author(s):  
Krishna Kumar Yadav, Dr. Anurag Sharma, Dr. Abhishek Badholia
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Correct Treatment ◽  
Related Disease ◽  
Huge Data ◽  
Learning Techniques ◽  
Supervised Learning Algorithms ◽  
Life Threatening

In few previous decades around the globe the reason for extensive number of deaths is cardiovascular disease or Heart related disease and not only in India but all around the world has emerged as a life-threatening disease. So for the correct treatment and in time diagnosis for this disease the need of feasible, accurate and reliable system is encountered. For automation of analysis of the sophisticated and huge data, to the various medical dataset of Machine Learning techniques and methods are applied. In recent times many researchers for the health care industry assistance with the help of various techniques of Machine Learning, this in turn helps the professionals in the procedure of the heart related disease diagnosis. A survey of various models that accepts such techniques and algorithms and their performance analysis is presented in this paper. Within the researchers few very fashionable Model supported supervised learning algorithms are Random forest (RF), Decision Tree (DT), Naïve Bayes, ensemble models, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM).  

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

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 of 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. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

Theoretical Approaches of Machine Learning to Schizophrenia

2018 ◽  
Vol 6 (2) ◽  
pp. 155-168 ◽  
Author(s):  
Naresh Babu Bynagari ◽  
Takudzwa Fadziso
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Healthy Controls ◽  
Theoretical Approaches ◽  
Automatic Categorization ◽  
Learning Techniques ◽  
Key Characteristics ◽  
Neuroimaging Data

Machine learning techniques have been successfully used to analyze neuroimaging data in the context of disease diagnosis in recent years. In this study, we present an overview of contemporary support vector machine-based methods developed and used in psychiatric neuroimaging for schizophrenia research. We focus in particular on our group's algorithms, which have been used to categorize schizophrenia patients and healthy controls, and compare their accuracy findings to those of other recently published studies. First, we'll go over some basic pattern recognition and machine learning terms. Then, for each study, we describe and discuss it independently, emphasizing the key characteristics that distinguish each approach. Finally, conclusions are reached as a result of comparing the data obtained using the various methodologies presented to determine how beneficial automatic categorization systems are in understanding the molecular underpinnings of schizophrenia. The primary implications of applying these approaches in clinical practice are then discussed.

scholarly journals Machine Learning Based Method for Prediction of Heart Disease in Big Data Environment

2020 ◽  
Vol 9 (4) ◽  
pp. 1917-1921
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Historical Data ◽  
Heart Diseases ◽  
Medical Professional ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Huge Data ◽  
Learning Techniques ◽  
Non Linear

Prediction of diseases is one of the challenging tasks in healthcare domain. Conventionally the heart diseases were diagnosed by experienced medical professional and cardiologist with the help of medical and clinical tests. With conventional method even experienced medical professional struggled to predict the disease with sufficient accuracy. In addition, manually analysing and extracting useful knowledge from the archived disease data becomes time consuming as well as infeasible. The advent of machine learning techniques enables the prediction of various diseases in healthcare domain. Machine learning algorithms are trained to learn from the existing historical data and prediction models are being created to predict the unknown raw data. For the past two decades, machine learning techniques are extensively employed for disease prediction. Despite the capability of machine algorithm on learning from huge historical data which is stored in data mart and data warehouses using traditional database technologies such as Oracle OnLine Analytical Processing (OLAP). The conventional database technologies suffer from the limitation that they cannot handle huge data or unstructured data or data that comes with speed. In this context, big data tools and technologies plays a major role in storing and facilitating the processing of huge data. In this paper, an approach is proposed for prediction of heart diseases using Support Vector Algorithm in Spark environment. Support Vector Machine algorithm is basically a binary classifier which classifies both linear and non-linear input data. It transforms the non-linear data into hyper plan with the help of different kernel functions. Spark is a distributed big data processing platform which has a unique feature of keeping and processing a huge data in memory. The proposed approach is tested with a benchmark dataset from UCI repository and results are discussed.

scholarly journals Performance Evaluation of Several Machine Learning Techniques Used in the Diagnosis of Mammograms

2019 ◽  
Vol 8 (10) ◽  
pp. 228-232 ◽  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Theoretical Research ◽  
Support Vector ◽  
Common Disease ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Analysis Society ◽  
Life Threatening

Throughout the world breast cancer has become a common disease among the women and it is also a life threatening diseases. Machine learning(ML) approach has been widely used for the diagnosis of benign and malignant masses in the mammogram. In this manuscript, I have represented the theoretical research and practical advances on various machine learning techniques the diagnosis of benign and malignant masses in the mammogram. The objective of this manuscript is to analyze the performance of distinct machine learning techniques used in the diagnosis of the Digital Mammography Image Analysis Society (MIAS) database. In this work I have compared performance of four machine learning approaches i.e. Support Vector, Naive Bayes, K-Nearest Neighbours and Multilayer Perceptron. The above four types of machine learning algorithm are used to categorize mammograms image. The achievements of these four techniques were recognized to discover the most acceptable classifier. On the end of the examine, derived outcomes indicates that support vector is a successful approach compares to other approach.

scholarly journals A Comparative study of machine learning algorithms on thyroid disease prediction

2018 ◽  
Vol 7 (2.8) ◽  
pp. 315 ◽  
Author(s):  
Shaik Razia ◽  
P SwathiPrathyusha ◽  
N Vamsi Krishna ◽  
N Sathya Sumana
Keyword(s):  
Machine Learning ◽  
Thyroid Hormones ◽  
Thyroid Disease ◽  
High Capacity ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Learning Techniques

Thyroid illness is a medicinal state that influences the functionality of the thyroid organ that is thyroid gland [1](Guyton, 2011).The indications of thyroid ailment differ basing upon the type. There are four most common varieties: hypothyroidism (low capacity) which is caused due to the insufficiency of the thyroid hormones; hyperthyroidism (high capacity) which is caused due to the existence of the thyroid hormones more than just sufficient, basic variations from the norm, most normally an augmentation of the thyroid organ; and tumors which can be benign or can cause cancer. It is additionally conceivable to have irregular thyroid capacity tests with no clinical side effects [2](Bauer & al, 2013).In this study a comparative thyroid disease diagnosis were performed by using Machine learning techniques that is Support Vector Machine (SVM), Multiple Linear Regression, Naïve Bayes, Decision Trees. For this purpose, thyroid disease dataset gathered from the UCI machine learning database was used.

A QUALITATIVE LITERATURE REVIEW OF MACHINE LEARNING TECHNIQUES USED FOR DIAGNOSIS OF NEONATAL DISEASES IN HEALTHCARE

2021 ◽  
pp. 4-7
Author(s):  
Madhura Ranade ◽  
Anupama Deshpande
Keyword(s):  
Machine Learning ◽  
Treatment Plan ◽  
Disease Diagnosis ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Detection ◽  
Neonatal Disease ◽  
Learning Techniques ◽  
Prediction And Prevention

Background:There has been signicant growth in the use of Articial Intelligence (AI) for healthcare in the last decade. Aim: To identify effective AI techniques for the prediction & diagnosis of neonatal diseases and preventive measures & treatment plan for them. Neonates are newborn babies less than a month old. Methods:Research papers published in databases like IEEE Xplore, Medline, PUBMED and Elsevier were searched to nd publications reporting the application of AI for the prediction and prevention of neonatal diseases. The overall search strategy was to retrieve articles that included terms that were related to “NICU”, “Articial Intelligence”, “Neonatal diseases” and “Healthcare”. Results: Hundreds of papers were identied in initial search, out of which 13 publications met the evaluation criteria of related terms inclusion, AI for Neonatal Diseases in particular. These papers described application of AI techniques in neonatal healthcare for disease detection and were summarized for nal analysis. Most of the papers are focused on using supervised machine learning techniques for the prediction of diseases. Various other approaches in AI techniques used in neonatal disease diagnosis have been tested for related ndings, factors, methods, to address and document performance metrics. The comparative analysis of ML model evaluation parameters like AUC (Area under Curve), Specicity, Sensitivity, True Positive and False-negative Rates was done to develop the scope for improving performance of AI/MLtechniques. Conclusion: The systematic study and review of different AI techniques such as supervised machine learning; articial neural networks, data mining techniques used for neonatal disease diagnosis highlighted their role in disease prediction, management, and treatment plan. More studies are needed to improve the use of AI for timely prediction of neonatal diseases like respiratory distress syndrome, sepsis for increasing the survival chances in preterm or normal neonates. The supervised learning models like Support Vector Machines(SVM), Decision Trees, K nearest neighbors are found to be effective for neonatal disease detection and will be applied in future research.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

A sentiment analysis system for social media using machine learning techniques: Social enablement

2018 ◽  
Vol 34 (3) ◽  
pp. 569-581 ◽  
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Tool ◽  
Data Set ◽  
Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

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