scholarly journals Ontology Based System for Prediction of Diseases

2020 ◽  
pp. 277-285
Author(s):  
Pallavi Laxmikant Chavan ◽  
Mandar S. Karyakarte
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Accurate Result ◽  
Learning Algorithm ◽  
Disease Outbreak ◽  
Prediction Algorithm ◽  
Main Role ◽  
Huge Amount ◽  
Large Cities ◽  
Increasing Risk

<p>In today’s world of lifestyle, biomedical and healthcare act the main role through which disease in the patient can be identified. However, the current solution focuses on communities where the accurate prediction plays a major role to find out risk of the disease in the patient. The detection of disease is done by using prediction algorithm. Here, machine-learning algorithm is has been used to find the accuracy. The dataset has been is collected from certain hospitals and pre-processed where the missing values have been reconstructed before prediction process. Due to the huge amount of information in healthcare, the accurate result is the need for disease recognition and services. Generally raw data has bad quality because it does have exactness, completeness of records fields. Moreover, there would be different exhibits in different regions, the appearances of certain diseases, which may also weaken the prediction of the disease outbreak. Using the health record, our system received the rate of accuracy is 97%. In this proposed system, we provides prediction of various diseases that occurs through using machine learning that will be effective. In urban lifestyle, modern large cities have significant adverse effects on health, & increasing risk of diseases.</p>

A Novel Machine Learning Sepsis Prediction Algorithm for Intended ICU Use (NAVOY Sepsis): A Proof-of-Concept Study (Preprint)

2021 ◽  
Author(s):  
Inger Persson ◽  
Andreas Östling ◽  
Martin Arlbrandt ◽  
Joakim Söderberg ◽  
David Becedas
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Learning Algorithm ◽  
Scoring Systems ◽  
High Accuracy ◽  
Prediction Algorithm ◽  
Massachusetts Institute Of Technology ◽  
Operating Characteristics ◽  
Mortality And Morbidity ◽  
Institute Of Technology

BACKGROUND Despite decades of research, sepsis remains a leading cause of mortality and morbidity in ICUs worldwide. The key to effective management and patient outcome is early detection, where no prospectively validated machine learning prediction algorithm is available for clinical use in Europe today. OBJECTIVE To develop a high-performance machine learning sepsis prediction algorithm based on routinely collected ICU data, designed to be implemented in Europe. METHODS The machine learning algorithm is developed using Convolutional Neural Network, based on the Massachusetts Institute of Technology Lab for Computational Physiology MIMIC-III Clinical Database, focusing on ICU patients aged 18 years or older. Twenty variables are used for prediction, on an hourly basis. Onset of sepsis is defined in accordance with the international Sepsis-3 criteria. RESULTS The developed algorithm NAVOY Sepsis uses 4 hours of input and can with high accuracy predict patients with high risk of developing sepsis in the coming hours. The prediction performance is superior to that of existing sepsis early warning scoring systems, and competes well with previously published prediction algorithms designed to predict sepsis onset in accordance with the Sepsis-3 criteria, as measured by the area under the receiver operating characteristics curve (AUROC) and the area under the precision-recall curve (AUPRC). NAVOY Sepsis yields AUROC = 0.90 and AUPRC = 0.62 for predictions up to 3 hours before sepsis onset. The predictive performance is externally validated on hold-out test data, where NAVOY Sepsis is confirmed to predict sepsis with high accuracy. CONCLUSIONS An algorithm with excellent predictive properties has been developed, based on variables routinely collected at ICUs. This algorithm is to be further validated in an ongoing prospective randomized clinical trial and will be CE marked as Software as a Medical Device, designed for commercial use in European ICUs.

scholarly journals Prediction of Acute Kidney Injury with a Machine Learning Algorithm using Electronic Health Record Data

2017 ◽  
Author(s):  
Hamid Mohamadlou ◽  
Anna Lynn-Palevsky ◽  
Christopher Barton ◽  
Uli Chettipally ◽  
Lisa Shieh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Sofa Score ◽  
Learning Algorithm ◽  
Medical Center ◽  
Kidney Injury ◽  
Kidney Damage ◽  
Prediction Algorithm ◽  
Prediction Tool ◽  
Electronic Health Record Data

AbstractBackgroundA major problem in treating acute kidney injury (AKI) is that clinical criteria for recognition are markers of established kidney damage or impaired function; treatment before such damage manifests is desirable. Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient AKI and those at high risk of developing AKI could be identified.MethodsWe used a machine learning technique, boosted ensembles of decision trees, to train an AKI prediction tool on retrospective data from inpatients at Stanford Medical Center and intensive care unit patients at Beth Israel Deaconess Medical Center. We tested the algorithm’s ability to detect AKI at onset, and to predict AKI 12, 24, 48, and 72 hours before onset, and compared its 3-fold cross-validation performance to the SOFA score for AKI identification in terms of Area Under the Receiver Operating Characteristic (AUROC).ResultsThe prediction algorithm achieves AUROC of 0.872 (95% CI 0.867, 0.878) for AKI onset detection, superior to the SOFA score AUROC of 0.815 (P < 0.01). At 72 hours before onset, the algorithm achieves AUROC of 0.728 (95% CI 0.719, 0.737), compared to the SOFA score AUROC of 0.720 (P < 0.01).ConclusionsThe results of these experiments suggest that a machine-learning-based AKI prediction tool may offer important prognostic capabilities for determining which patients are likely to suffer AKI, potentially allowing clinicians to intervene before kidney damage manifests.

scholarly journals Prediction of Tissue Damage Using a User-Independent Machine Learning Algorithm vs. Tmax Threshold Maps

2021 ◽  
Vol 5 (3) ◽  
pp. 21
Author(s):  
Arsany Hakim ◽  
Benjamin Messerli ◽  
Raphael Meier ◽  
Tomas Dobrocky ◽  
Sebastian Bellwald ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arterial Input Function ◽  
Learning Algorithm ◽  
Infarct Volume ◽  
Estimation Algorithm ◽  
Input Function ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Related Factors ◽  
Decision Forest

(1) Background: To test the accuracy of a fully automated stroke tissue estimation algorithm (FASTER) to predict final lesion volumes in an independent dataset in patients with acute stroke; (2) Methods: Tissue-at-risk prediction was performed in 31 stroke patients presenting with a proximal middle cerebral artery occlusion. FDA-cleared perfusion software using the AHA recommendation for the Tmax threshold delay was tested against a prediction algorithm trained on an independent perfusion software using artificial intelligence (FASTER). Following our endovascular strategy to consequently achieve TICI 3 outcome, we compared patients with complete reperfusion (TICI 3) vs. no reperfusion (TICI 0) after mechanical thrombectomy. Final infarct volume was determined on a routine follow-up MRI or CT at 90 days after the stroke; (3) Results: Compared to the reference standard (infarct volume after 90 days), the decision forest algorithm overestimated the final infarct volume in patients without reperfusion. Underestimation was observed if patients were completely reperfused. In cases where the FDA-cleared segmentation was not interpretable due to improper definitions of the arterial input function, the decision forest provided reliable results; (4) Conclusions: The prediction accuracy of automated tissue estimation depends on (i) success of reperfusion, (ii) infarct size, and (iii) software-related factors introduced by the training sample. A principal advantage of machine learning algorithms is their improved robustness to artifacts in comparison to solely threshold-based model-dependent software. Validation on independent datasets remains a crucial condition for clinical implementations of decision support systems in stroke imaging.

scholarly journals Implementation of XGBoost Ensemble Learning Model for Detecting Money Laundering

2021 ◽  
Vol 9 (VII) ◽  
pp. 312-316
Author(s):  
Abarna Ramprakash
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Money Laundering ◽  
Learning Algorithm ◽  
Learning Model ◽  
False Positives ◽  
Machine Learning Algorithm ◽  
Huge Amount ◽  
Rule Based ◽  
Complex Sequence

Money laundering is the illegal process of concealing the origins of money obtained illegally by passing it through a complex sequence of banking transfers. Currently banks use rule based systems to identify the suspicious transactions which could be used for money laundering. However these systems generate a large number of false positives which leads the banks to spend a huge amount of money and time in investigating the false positives. Hence, in this paper, the monitoring of transactions is to be done using XGBoost machine learning algorithm in order to reduce the number of false positives and to increase the probability of identifying true positives.

Cooking Procedure Recognition and Support by Ubiquitous Sensors

2009 ◽  
Vol 21 (4) ◽  
pp. 498-506 ◽  
Author(s):  
Sho Murakami ◽  
◽  
Takuo Suzuki ◽  
Akira Tokumasu ◽  
Yasushi Nakauchi
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
User Behavior ◽  
Learning Algorithm ◽  
Experimental Results ◽  
Prediction Algorithm ◽  
Machine Learning Algorithm ◽  
Behavior Prediction ◽  
Model Based ◽  
Sensor Information

This paper proposes cooking support using ubiquitous sensors. We developed a machine learning algorithm that recognizes cooking procedures by taking into account widely varying sensor information and user behavior. To provide appropriate instructions to users, we developed a Markov-model-based behavior prediction algorithm. Using these algorithms, we developed cooking support automatically displaying cooking instruction videos based on user progress. Experiments and experimental results confirmed the feasibility of our proposed cooking support.

scholarly journals A machine-learning approach to predict postprandial hypoglycemia

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Wonju Seo ◽  
You-Bin Lee ◽  
Seunghyun Lee ◽  
Sang-Man Jin ◽  
Sung-Min Park
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Artificial Pancreas ◽  
Individual Performance ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Support Vector ◽  
K Nearest Neighbor

Abstract Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.

scholarly journals Exploring the Machine Learning Algorithm for Prediction the Loan Sanctioning Process

2019 ◽  
Vol 9 (1) ◽  
pp. 2714-2719
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Accurate Result ◽  
Learning Algorithm ◽  
Experimental Tests ◽  
Safe Procedure ◽  
Bayes Model ◽  
The Past ◽  
Machine Learning Model ◽  
Number Of Customers

Extending credits to corporates and individuals for the smooth functioning of growing economies like India is inevitable. As increasing number of customers apply for loans in the banks and non- banking financial companies (NBFC), it is really challenging for banks and NBFCs with limited capital to device a standard resolution and safe procedure to lend money to its borrowers for their financial needs. In addition, in recent times NBFC inventories have suffered a significant downfall in terms of the stock price. It has contributed to a contagion that has also spread to other financial stocks, adversely affecting the benchmark in recent times. In this paper, an attempt is made to condense the risk involved in selecting the suitable person who could repay the loan on time thereby keeping the bank’s non-performing assets (NPA) on the hold. This is achieved by feeding the past records of the customer who acquired loans from the bank into a trained machine learning model which could yield an accurate result. The prime focus of the paper is to determine whether or not it will be safe to allocate the loan to a particular person. This paper has the following sections (i) Collection of Data, (ii) Data Cleaning and (iii) Performance Evaluation. Experimental tests found that the Naïve Bayes model has better performance than other models in terms of loan forecasting.

scholarly journals Machine learning for predictions of cervical cancer identification – preliminary investigation based on refractive index

2021 ◽  
Author(s):  
Michał Kruczkowski ◽  
Anna Drabik-Kruczkowska ◽  
Anna Marciniak ◽  
Martyna Tarczewska ◽  
Monika Kosowska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Refractive Index ◽  
Early Diagnosis ◽  
Learning Algorithm ◽  
Optical Measurements ◽  
Prediction Algorithm ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting

Abstract Cervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors – to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors in the early diagnosis stage of cervical cancer. We demonstrate the preliminary research on cervical cancer assessment utilizing optical sensor and prediction algorithm. Since each matter is characterized by refractive index, measuring its value and detecting changes give information about the state of the tissue. The optical measurements provided datasets for training and validating the analyzing software. We present data preprocessing, machine learning results utilizing three algorithms (Random Forest, eXtreme Gradient Boosting, Naïve Bayes) and assessment of their performance for classification of tissue as healthy or sick. All of them provided high values (>89%) of the measures describing them. Our solution allows for rapid sample measurement and automatic classification of the results constituting a potential support tool for doctors.

scholarly journals DeepKhib: a deep-learning framework for lysine 2-hydroxyisobutyrylation sites prediction

2020 ◽  
Author(s):  
Luna Zhang ◽  
Yang Zou ◽  
Ningning He ◽  
Yu Chen ◽  
Zhen Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Learning Models ◽  
Post Translational Modification ◽  
Deep Learning Algorithm ◽  
Multiple Species ◽  
Species Specific

AbstractAs a novel type of post-translational modification, lysine 2-Hydroxyisobutyrylation (Khib) plays an important role in gene transcription and signal transduction. In order to understand its regulatory mechanism, the essential step is the recognition of Khib sites. Thousands of Khib sites have been experimentally verified across five different species. However, there are only a couple traditional machine-learning algorithms developed to predict Khib sites for limited species, lacking a general prediction algorithm. We constructed a deep-learning algorithm based on convolutional neural network with the one-hot encoding approach, dubbed CNNOH. It performs favorably to the traditional machine-learning models and other deep-learning models across different species, in terms of cross-validation and independent test. The area under the ROC curve (AUC) values for CNNOH ranged from 0.82 to 0.87 for different organisms, which is superior to the currently-available Khib predictors. Moreover, we developed the general model based on the integrated data from multiple species and it showed great universality and effectiveness with the AUC values in the range of 0.79 to 0.87. Accordingly, we constructed the on-line prediction tool dubbed DeepKhib for easily identifying Khib sites, which includes both species-specific and general models. DeepKhib is available at http://www.bioinfogo.org/DeepKhib.

scholarly journals Real-time credible online health information inquiring: a novel search engine misinformation notifier extension (SEMiNExt) during COVID-19-like disease outbreak

2020 ◽  
Author(s):  
Abdullah Bin Shams ◽  
Ehsanul Hoque Apu ◽  
Ashiqur Rahman ◽  
Nazeeba Siddika ◽  
Mohsin Sarker Raihan ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Real Time ◽  
Search Engine ◽  
Learning Algorithm ◽  
Disease Outbreak ◽  
Data Set ◽  
User Query ◽  
Tree Classifier ◽  
Health Related

Abstract Public health-related misinformation spread rapidly in online networks, particularly, in social media during any disease outbreak. Misinformation of coronavirus disease 2019 (COVID-19) drug protocol or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities are utilizing several surveillance tools to detect, and slow down the rapid misinformation spread online, still millions of misinformation are found online. However, there is no currently available tool for receiving real-time misinformation notification during online health or COVID-19 related inquiries. Our proposed novel combinational approach, where we have integrated machine learning techniques with novel search engine misinformation notifier extension (SEMiNExt), helps to understand which news or information is from unreliable sources in real-time. The extension filters the search results and shows notification beforehand; it is a new and unexplored approach to prevent the spread of misinformation. To validate the user query, SEMiNExt transfers the data to a machine learning algorithm or classifier which predicts the authenticity of the search inquiry and sends a binary decision as either true or false. The results show that the supervised learning algorithm works best when 80% of the data set have been used for training purpose. Also, 10-fold cross-validation demonstrate a maximum accuracy and F1-score of 84.3% and 84.1% respectively for the Decision Tree classifier while the K-nearest-neighbor (KNN) algorithm shows the least performance. The SEMiNExt approach has introduced the possibility to improve online health communication system by showing misinformation notifications in real-time which enables safer web-based searching while inquiring on health-related issues.

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