scholarly journals Stroke Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 1518-1523
Author(s):  
R. Bhavana
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Actual State ◽  
Support Vector ◽  
Clinical Report ◽  
Way Of Life ◽  
Current Time ◽  
Characterization Result ◽  
The Third ◽  
The World

Stroke is a drawn out inability sickness caused everywhere on the world and it is the third driving reason for demise. Early forecast of stroke gives more important to the current time. Stroke happens fundamentally due to individuals' way of life in the advanced time changing elements, for example, high glucose, coronary illness, weight, diabetes. In this examination, we analyze the Support vector machine, Decision tree, Random forest and XG Boost.we have utilized four AI calculations to recognize the sort of stroke that can happen or happened to structure an individual's actual state and clinical report information. We have gathered a decent number of sections from the clinics and use them to take care of our concern. The characterization result shows that the outcome is good and can be utilized continuously clinical report. We accept that AI calculations can help better comprehension of illnesses and can be a decent medical care buddy.

scholarly journals Stock Market Prediction using Machine Learning and Cloud Computing

2019 ◽  
Vol 8 (09) ◽  
pp. 24847-24850
Author(s):  
Nirbhay Narkhede
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cloud Computing ◽  
Crucial Role ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Computational Power ◽  
The World ◽  
A Company ◽  
Risky Business

In the world with increasing globalization , where money places a crucial role in determining the expansion and earnings of a company trading places a very crucial role. Multiple companies invest millions and billions of dollars in other countries with an expectation to make profits. In such a risky business Predicting the movement of the market can help companies or individual in making good decisions and can prevent severe loses. In this research paper we will discuss how we can use the computational power of the computer on cloud along with the machine learning algorithms to predict the closing values of the stocks which is a big challenge otherwise. For this purpose we will use Python as our programming language which supports a lot of ML based Libraries. The models we will be using are SVM(Support Vector Machine) , Linear Regression , Random Forest, XGBoost ,LSTM for deep learning

scholarly journals COVID-19 Future Predictions Using 4 Supervised Machine Learning Models

2022 ◽  
pp. 61-66
Author(s):  
Aditi Vadhavkar ◽  
Pratiksha Thombare ◽  
Priyanka Bhalerao ◽  
Utkarsha Auti
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support Vector Machine ◽  
Supervised Machine Learning ◽  
Perioperative Outcomes ◽  
Support Vector ◽  
Learning Models ◽  
Death Rates ◽  
The World ◽  
Machine Learning Models

Forecasting Mechanisms like Machine Learning (ML) models having been proving their significance to anticipate perioperative outcomes in the domain of decision making on the future course of actions. Many application domains have witnessed the use of ML models for identification and prioritization of adverse factors for a threat. The spread of COVID-19 has proven to be a great threat to a mankind announcing it a worldwide pandemic throughout. Many assets throughout the world has faced enormous infectivity and contagiousness of this illness. To look at the figure of undermining components of COVID-19 we’ve specifically used four Machine Learning Models Linear Regression (LR), Least shrinkage and determination administrator (LASSO), Support vector machine (SVM) and Exponential smoothing (ES). The results depict that the ES performs best among the four models employed in this study, followed by LR and LASSO which performs well in forecasting the newly confirmed cases, death rates yet recovery rates, but SVM performs poorly all told the prediction scenarios given the available dataset.

scholarly journals World Covid-19 Vaccine Names Classification Using Neural Network Method

2021 ◽  
Vol 1 (1) ◽  
pp. 31
Author(s):  
Kristiawan Nugroho
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Neural Network Method ◽  
The Public ◽  
Machine Learning Methods ◽  
The Neural Network ◽  
The World ◽  
Network Method

The Covid-19 pandemic has occurred for a year on earth. Various attempts have been made to overcome this pandemic, especially in making various types of vaccines developed around the world. The level of vaccine effectiveness in dealing with Covid-19 is one of the questions that is often asked by the public. This research is an attempt to classify the names of vaccines that have been used in various nations by using one of the robust machine learning methods, namely the Neural Network. The results showed that the Neural Network method provides the best accuracy, which is 99.9% higher than the Random Forest and Support Vector Machine(SVM) methods.

scholarly journals Discrimination of SARS-Cov 2 and arboviruses (DENV, ZIKV and CHIKV) clinical features using machine learning techniques: a fast and inexpensive clinical screening for countries simultaneously affected by both diseases

2021 ◽  
Author(s):  
João Daniel S. Castro
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Clinical Screening ◽  
Learning Techniques ◽  
The World ◽  
Area Under Roc Curve

AbstractSARS-Cov-2 (Covid-19) has spread rapidly throughout the world, and especially in tropical countries already affected by outbreaks of arboviruses, such as Dengue, Zika and Chikungunya, and may lead these locations to a collapse of health systems. Thus, the present work aims to develop a methodology using a machine learning algorithm (Support Vector Machine) for the prediction and discrimination of patients affected by Covid-19 and arboviruses (DENV, ZIKV and CHIKV). Clinical data from 204 patients with both Covid-19 and arboviruses obtained from 23 scientific articles and 1 dataset were used. The developed model was able to predict 93.1% of Covid-19 cases and 82.1% of arbovirus cases, with an accuracy of 89.1% and Area under Roc Curve of 95.6%, proving to be effective in prediction and possible screening of these patients, especially those affected by Covid-19, allowing early isolation.

Analysis of Heart Disorder by Using Machine Learning Methods and Data Mining Techniques

2021 ◽  
pp. 212-221
Author(s):  
Sarangam Kodati ◽  
Jeeva Selvaraj
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Heart Diseases ◽  
Support Vector ◽  
Primary Reason ◽  
The World ◽  
Mining Methods ◽  
Using Data

Data mining is the most famous knowledge extraction approach for knowledge discovery from data (KDD). Machine learning is used to enable a program to analyze data, recognize correlations, and make usage on insights to solve issues and/or enrich data and because of prediction. The chapter highlights the need for more research within the usage of robust data mining methods in imitation of help healthcare specialists between the diagnosis regarding heart diseases and other debilitating disease conditions. Heart disease is the primary reason of death of people in the world. Nearly 47% of death is caused by heart disease. The authors use algorithms including random forest, naïve Bayes, support vector machine to analyze heart disease. Accuracy on the prediction stage is high when using a greater number of attributes. The goal is to function predictive evaluation using data mining, using data mining to analyze heart disease, and show which methods are effective and efficient.

AUTOMATIC ARRHYTHMIA DETECTION BASED ON THE ANALYSIS OF ELECTROCARDIOGRAMS WITH DEEP LEARNING

2021 ◽  
pp. 18-27
Author(s):  
E. Yu. Shchetinin
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Classification Accuracy ◽  
Automated Detection ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Ecg Signals ◽  
The World

According to the World Health Organization, cardiovascular diseases (CVD) are one of the most common causes of death in the world. The most effective clinical method for visualizing the cardiac electrical activity is electrocardiography (ECG). Automated ECG analysis has been of great interest in the medical researches. The problem of automated detection of cardiac arrhythmias may be reduced to the ECG signals classification. To solve this task such methods were used as Hidden Markov Models (HMM), discrete wavelet transforms (DWT), support vector machine (SVM) etc. Now days, the deep learning models began to play the major role in solving this problem. In this paper, for the classification of ECG signals, a number of models of deep neural networks, including deep convolutional, recurrent based on short-term long memory have been developed and implemented. To improve the classification accuracy of individual classes of the studied data, the CNN-LSTM deep model was built, which combines convolutional and recurrent networks. In addition the following machine learning algorithms were used for ECG signals classification: support vector machine (SVM), decision trees (DT), random forest (RF) and extreme gradient boosting (XGB). To test the performance of the proposed models, MIT-BIH database was used, a freely available dataset that is widely used to evaluate the effectiveness of ECG signal classification algorithms. The results of a comparative analysis of various algorithms for the quality of classification for individual classes showed that machine learning algorithms classify classes with a large volume of samples well. For example, SVM and DT classify samples from class N and Q with an accuracy of 92 and 97%, respectively, while samples from classes S and F are classified with much worse accuracy of 63%. At the same time, analyzing and comparing the performance of various neural network models based on the obtained estimates of the classification accuracy, it can be argued that CNN LSTM model allows not only a high classification accuracy of 99.37%, but also high values of other indicators of classification quality, such as F1- metric, precision, and recall.The proposed algorithms for the automated detection of cardiac arrhythmias can be applied in biomedical applications that analyze the electrocardiogram and help physicians diagnose cardiac arrhythmias more accurately.

scholarly journals Indian State or City Covid-19 Cases Outbreak Forecast utilizing Machine Learning Models

2021 ◽  
pp. 286-293
Author(s):  
Brijesh Patel ◽  
Dr. Sheshang Degadwala
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Perioperative Outcomes ◽  
Support Vector ◽  
Learning Models ◽  
Potential Threat ◽  
The World ◽  
Indian State ◽  
Long Time ◽  
Machine Learning Models

Several episode expectation models for COVID-19 are being used by officials all over the world to make informed decisions and maintain necessary control steps. AI (ML)-based deciding elements have proven their worth in forecasting perioperative outcomes in order to enhance the dynamic of the predicted course of activities. For a long time, ML models have been used in a variety of application areas that needed identifiable evidence and prioritization of unfavorable factors for a danger. To cope with expecting problems, a few anticipation strategies are commonly used. This study demonstrates the ability of ML models to predict the number of future patients affected by COVID-19, which is now regarded as a potential threat to humanity. In particular, four standard evaluating models, such as Linear Regression, Support Vector Machine, LASSO, Exponential Smoothing, and Decision Tree, were used in this investigation to hypothesis the compromising variables of COVID-19. Any one of the models makes three types of predictions, for example, the number of recently Positive cases after and before preliminary vexing, the amount of passing's after and before preliminary lockdown, and the number of recuperations after and before lockdown. The outcomes demonstrate with parameters like R2 Score, Adjust R2 score, MSE, MAE and RMSE on Indian datasets.

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

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