Analysis and Prediction Blood Pressure and Disease by Applying Decision Tree, Naïve Base and Random Forest algorithms

2020 ◽  
Keyword(s):  
Blood Pressure ◽  
Random Forest ◽  
Decision Tree

scholarly journals Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Majid Nour ◽  
Kemal Polat
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Random Forest ◽  
Decision Tree ◽  
Systolic Blood Pressure ◽  
Diastolic Blood Pressure ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
C4.5 Decision Tree

Hypertension (high blood pressure) is an important disease seen among the public, and early detection of hypertension is significant for early treatment. Hypertension is depicted as systolic blood pressure higher than 140 mmHg or diastolic blood pressure higher than 90 mmHg. In this paper, in order to detect the hypertension types based on the personal information and features, four machine learning (ML) methods including C4.5 decision tree classifier (DTC), random forest, linear discriminant analysis (LDA), and linear support vector machine (LSVM) have been used and then compared with each other. In the literature, we have first carried out the classification of hypertension types using classification algorithms based on personal data. To further explain the variability of the classifier type, four different classifier algorithms were selected for solving this problem. In the hypertension dataset, there are eight features including sex, age, height (cm), weight (kg), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), heart rate (bpm), and BMI (kg/m2) to explain the hypertension status and then there are four classes comprising the normal (healthy), prehypertension, stage-1 hypertension, and stage-2 hypertension. In the classification of the hypertension dataset, the obtained classification accuracies are 99.5%, 99.5%, 96.3%, and 92.7% using the C4.5 decision tree classifier, random forest, LDA, and LSVM. The obtained results have shown that ML methods could be confidently used in the automatic determination of the hypertension types.

scholarly journals Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 268-269
Author(s):  
Jaime Speiser ◽  
Kathryn Callahan ◽  
Jason Fanning ◽  
Thomas Gill ◽  
Anne Newman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Random Forest ◽  
Decision Tree ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Learning Methods ◽  
Life Study ◽  
Fall Injury ◽  
Machine Learning Methods

Abstract Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty understanding the complex algorithms behind models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated in data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). Machine learning methods may offer improved performance compared to traditional models for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.

scholarly journals Development of a Safety Management System Tracking the Weight of Heavy Objects Carried by Construction Workers Using FSR Sensors

2021 ◽  
Vol 11 (4) ◽  
pp. 1378
Author(s):  
Seung Hyun Lee ◽  
Jaeho Son
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Safety Management ◽  
Tracking System ◽  
Construction Workers ◽  
Gradient Boosting ◽  
Prototype System ◽  
Construction Site ◽  
Average Accuracy ◽  
Smart Safety

It has been pointed out that the act of carrying a heavy object that exceeds a certain weight by a worker at a construction site is a major factor that puts physical burden on the worker’s musculoskeletal system. However, due to the nature of the construction site, where there are a large number of workers simultaneously working in an irregular space, it is difficult to figure out the weight of the object carried by the worker in real time or keep track of the worker who carries the excess weight. This paper proposes a prototype system to track the weight of heavy objects carried by construction workers by developing smart safety shoes with FSR (Force Sensitive Resistor) sensors. The system consists of smart safety shoes with sensors attached, a mobile device for collecting initial sensing data, and a web-based server computer for storing, preprocessing and analyzing such data. The effectiveness and accuracy of the weight tracking system was verified through the experiments where a weight was lifted by each experimenter from +0 kg to +20 kg in 5 kg increments. The results of the experiment were analyzed by a newly developed machine learning based model, which adopts effective classification algorithms such as decision tree, random forest, gradient boosting algorithm (GBM), and light GBM. The average accuracy classifying the weight by each classification algorithm showed similar, but high accuracy in the following order: random forest (90.9%), light GBM (90.5%), decision tree (90.3%), and GBM (89%). Overall, the proposed weight tracking system has a significant 90.2% average accuracy in classifying how much weight each experimenter carries.

Prediction of cohesion and friction angle from well-logging data using decision tree and random forest

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Moaz Hiba ◽  
Ahmed Farid Ibrahim ◽  
Salaheldin Elkatatny ◽  
Abdulwahab Ali
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Friction Angle ◽  
Well Logging

scholarly journals Modified Decision Tree Technique for Ransomware Detection at Runtime through API Calls

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Faizan Ullah ◽  
Qaisar Javaid ◽  
Abdu Salam ◽  
Masood Ahmad ◽  
Nadeem Sarwar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Feature Vector ◽  
Machine Learning Algorithms ◽  
The Novel ◽  
Proposed Model ◽  
Testing Accuracy ◽  
Financial Losses

Ransomware (RW) is a distinctive variety of malware that encrypts the files or locks the user’s system by keeping and taking their files hostage, which leads to huge financial losses to users. In this article, we propose a new model that extracts the novel features from the RW dataset and performs classification of the RW and benign files. The proposed model can detect a large number of RW from various families at runtime and scan the network, registry activities, and file system throughout the execution. API-call series was reutilized to represent the behavior-based features of RW. The technique extracts fourteen-feature vector at runtime and analyzes it by applying online machine learning algorithms to predict the RW. To validate the effectiveness and scalability, we test 78550 recent malign and benign RW and compare with the random forest and AdaBoost, and the testing accuracy is extended at 99.56%.

scholarly journals Classification of Hypertensive and Normotensive Subjects Using Bilateral Differential Biopotential Signals

2021 ◽  
Author(s):  
Arindam Sarkar ◽  
Bhaswati Goswami ◽  
Ratna Ghosh
Keyword(s):  
Blood Pressure ◽  
Random Forest ◽  
High Blood Pressure ◽  
Pivotal Role ◽  
Modern World ◽  
Hypertensive Subject ◽  
Normotensive Subjects ◽  
Electrocardiogram Ecg ◽  
Day By Day

Abstract Hypertension or high blood pressure is a severe health issue in the modern world, especially in this pandemic scenario, that can cause many heart related diseases or even death, and it is increasing day by day. For this reason, a reliable, automatic and easy to use system for hypertensive subject detection is an important focus for the researchers. Biopotential signals can play a pivotal role in this regard. Though, few strategies were proposed based on electrocardiogram (ECG) or electrodermal (EDA) signals, but those require special circuitry, as well as trained persons. In this article, a method is proposed to classify hypertensive and normotensive subjects using differential biopotential signals. Neither special circuitry, nor much expertise is required for handling this system. It was assumed that progression of rest is dependent upon blood pressure. To serve the purpose, signals were acquired from both hypertensive and normotensive subjects bilaterally for 10 continuous minutes. Result of the random forest (RF) classification establishes that from the analysis of the progression of the bilaterally acquired differential biopotential signals, hypertensive subjects can be distinguished from normotensive subjects.

scholarly journals Deteksi Gempa Berdasarkan Data Twitter Menggunakan Decision Tree, Random Forest, dan SVM

2017 ◽  
Vol 6 (1) ◽  
Author(s):  
Rendra Dwi Lingga P. ◽  
Chastine Fatichah ◽  
Diana Purwitasari
Keyword(s):  
Random Forest ◽  
Decision Tree
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