Detection of common risk factors for diagnosis of cardiac arrhythmia using machine learning algorithm

Abstract Background Wearable smart watches provide large amount of real-time data on the environmental state of the users and are useful to determine risk factors for onset and progression of myopia. We aim to evaluate the efficacy of machine learning algorithm in differentiating indoor and outdoor locations as collected by use of smart watches. Methods Real time data on luminance, ultraviolet light levels and number of steps obtained with smart watches from dataset A: 12 adults from 8 scenes and manually recorded true locations. 70% of data was considered training set and support vector machine (SVM) algorithm generated using the variables to create a classification system. Data collected manually by the adults was the reference. The algorithm was used for predicting the location of the remaining 30% of dataset A. Accuracy was defined as the number of correct predictions divided by all. Similarly, data was corrected from dataset B: 172 children from 3 schools and 12 supervisors recorded true locations. Data collected by the supervisors was the reference. SVM model trained from dataset A was used to predict the location of dataset B for validation. Finally, we predicted the location of dataset B using the SVM model self-trained from dataset B. We repeated these three predictions with traditional univariate threshold segmentation method. Results In both datasets, SVM outperformed the univariate threshold segmentation method. In dataset A, the accuracy and AUC of SVM were 99.55% and 0.99 as compared to 95.11% and 0.95 with the univariate threshold segmentation (p < 0.01). In validation, the accuracy and AUC of SVM were 82.67% and 0.90 compared to 80.88% and 0.85 with the univariate threshold segmentation method (p < 0.01). In dataset B, the accuracy and AUC of SVM and AUC were 92.43% and 0.96 compared to 80.88% and 0.85 with the univariate threshold segmentation (p < 0.01). Conclusions Machine learning algorithm allows for discrimination of outdoor versus indoor environments with high accuracy and provides an opportunity to study and determine the role of environmental risk factors in onset and progression of myopia. The accuracy of machine learning algorithm could be improved if the model is trained with the dataset itself.

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Machine Learning Approach to Classify Cardiovascular Disease in Patients With Nonalcoholic Fatty Liver Disease in the UK Biobank Cohort

Journal of the American Heart Association ◽

10.1161/jaha.121.022576 ◽

2021 ◽

Author(s):

Divya Sharma ◽

Neta Gotlieb ◽

Michael E. Farkouh ◽

Keyur Patel ◽

Wei Xu ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Liver Disease ◽

Genetic Risk ◽

Fatty Liver Disease ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Uk Biobank ◽

Nonalcoholic Fatty Liver ◽

The Uk

Background Nonalcoholic fatty liver disease (NAFLD) is the most prevalent liver disease worldwide. Cardiovascular disease (CVD) is the leading cause of mortality among patients with NAFLD. The aim of our study was to develop a machine learning algorithm integrating clinical, lifestyle, and genetic risk factors to identify CVD in patients with NAFLD. Methods and Results We created a cohort of patients with NAFLD from the UK Biobank, diagnosed according to proton density fat fraction from magnetic resonance imaging data sets. A total of 400 patients with NAFLD with subclinical atherosclerosis or clinical CVD, defined by disease codes, constituted cases and 446 NAFLD cases with no CVD constituted controls. We evaluated 7 different supervised machine learning approaches on clinical, lifestyle, and genetic variables for identifying CVD in patients with NAFLD. The most significant clinical and lifestyle variables observed by the predictive modeling were age (59 years [54.00–63.00 years]), hypertension (145 mm Hg [134.0–156.0 mm Hg] and 85 mm Hg [79.00–93.00 mm Hg]), waist circumference (98 cm [95.00–105.00 cm]), and sedentary lifestyle, defined as time spent watching TV >4 h/d. In the genetic data, single‐nucleotide polymorphisms in IL16 and ANKLE1 gene were most significant. Our proposed ensemble‐based integrative machine learning model achieved an area under the curve of 0.849 using the random forest modeling for CVD prediction. Conclusions We propose a machine learning algorithm that identifies CVD in patients with NAFLD through integration of significant clinical, lifestyle, and genetic risk factors. These patients with NAFLD at higher risk of CVD should be flagged for screening and aggressive treatment of their cardiometabolic risk factors to prevent cardiovascular morbidity and mortality.

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Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

10.1055/s-0038-1660728 ◽

2018 ◽

Author(s):

C.H.B. van Niftrik ◽

F. van der Wouden ◽

V. Staartjes ◽

J. Fierstra ◽

M. Stienen ◽

...

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Tumor Surgery ◽

Early Complications ◽

Brain Tumor Surgery

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Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

2020 ◽

pp. 216-222

Author(s):

Kunal Parikh ◽

Tanvi Makadia ◽

Harshil Patel

Keyword(s):

Public Health ◽

Machine Learning ◽

Developing Countries ◽

Feature Extraction ◽

Predictive Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Health Concerns ◽

The World ◽

Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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