Preliminary Cardiac Disease Risk Prediction Based on Medical and Behavioural Data Set Using Supervised Machine Learning Techniques

Drilling is a key task in exploration campaigns to characterize mineral deposits at depth. Drillcores are first logged in the field by a geologist and with regards to, e.g., mineral assemblages, alteration patterns, and structural features. The core-logging information is then used to locate and target the important ore accumulations and select representative samples that are further analyzed by laboratory measurements (e.g., Scanning Electron Microscopy (SEM), Xray diffraction (XRD), X-ray Fluorescence (XRF)). However, core-logging is a laborious task and subject to the expertise of the geologist. Hyperspectral imaging is a non-invasive and non-destructive technique that is increasingly being used to support the geologist in the analysis of drill-core samples. Nonetheless, the benefit and impact of using hyperspectral data depend on the applied methods. With this in mind, machine learning techniques, which have been applied in different research fields, provide useful tools for an advance and more automatic analysis of the data. Lately, machine learning frameworks are also being implemented for mapping minerals in drill-core hyperspectral data. In this context, this work follows an approach to map minerals on drill-core hyperspectral data using supervised machine learning techniques, in which SEM data, integrated with the mineral liberation analysis (MLA) software, are used in training a classifier. More specifically, the highresolution mineralogical data obtained by SEM-MLA analysis is resampled and co-registered to the hyperspectral data to generate a training set. Due to the large difference in spatial resolution between the SEM-MLA and hyperspectral images, a pre-labeling strategy is required to link these two images at the hyperspectral data spatial resolution. In this study, we use the SEM-MLA image to compute the abundances of minerals for each hyperspectral pixel in the corresponding SEM-MLA region. We then use the abundances as features in a clustering procedure to generate the training labels. In the final step, the generated training set is fed into a supervised classification technique for the mineral mapping over a large area of a drill-core. The experiments are carried out on a visible to near-infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data set and based on preliminary tests the mineral mapping task improves significantly.

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Cardiac Disease Prediction using Supervised Machine Learning Techniques.

Journal of Physics Conference Series ◽

10.1088/1742-6596/2161/1/012013 ◽

2022 ◽

Vol 2161 (1) ◽

pp. 012013

Author(s):

Chiradeep Gupta ◽

Athina Saha ◽

N V Subba Reddy ◽

U Dinesh Acharya

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Cardiac Disease ◽

Performance Metrics ◽

Confusion Matrix ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Ensemble Techniques ◽

Learning Techniques

Abstract Diagnosis of cardiac disease requires being more accurate, precise, and reliable. The number of death cases due to cardiac attacks is increasing exponentially day by day. Thus, practical approaches for earlier diagnosis of cardiac or heart disease are done to achieve prompt management of the disease. Various supervised machine learning techniques like K-Nearest Neighbour, Decision Tree, Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) model are used for predicting cardiac disease using a dataset that was collected from the repository of the University of California, Irvine (UCI). The results depict that Logistic Regression was better than all other supervised classifiers in terms of the performance metrics. The model is also less risky since the number of false negatives is low as compared to other models as per the confusion matrix of all the models. In addition, ensemble techniques can be approached for the accuracy improvement of the classifier. Jupyter notebook is the best tool, for the implementation of Python Programming having many types of libraries, header files, for accurate and precise work.

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Risk Prediction Analysis of Cardiovascular Disease Using Supervised Machine Learning Techniques

Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications ◽

10.1007/978-3-030-24051-6_41 ◽

2020 ◽

pp. 419-431

Author(s):

A. Ishwarya ◽

S. K. Jayanthi

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Risk Prediction ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Prediction Analysis ◽

Learning Techniques

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Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

Current Medicinal Chemistry ◽

10.2174/2213275912666191102162959 ◽

2020 ◽

Vol 28 (2) ◽

pp. 253-265 ◽

Cited By ~ 3

Author(s):

Gabriela Bitencourt-Ferreira ◽

Amauri Duarte da Silva ◽

Walter Filgueira de Azevedo

Keyword(s):

Machine Learning ◽

Binding Affinity ◽

Predictive Performance ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Scoring Functions ◽

Cyclin Dependent Kinase ◽

Learning Models ◽

Learning Techniques ◽

Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

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Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200904164539 ◽

2020 ◽

Vol 13 ◽

Author(s):

Ritu Khandelwal ◽

Hemlata Goyal ◽

Rajveer Singh Shekhawat

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Data Science ◽

Training Data ◽

Machine Learning Techniques ◽

Future Trends ◽

Data Set ◽

Learning Stage ◽

Learning Techniques ◽

Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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Local mortality estimates during the COVID-19 pandemic in Italy

Journal of Population Economics ◽

10.1007/s00148-021-00857-y ◽

2021 ◽

Author(s):

Augusto Cerqua ◽

Roberta Di Stefano ◽

Marco Letta ◽

Sara Miccoli

Keyword(s):

Machine Learning ◽

Excess Mortality ◽

Control Method ◽

Local Level ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Mortality Data ◽

Official Method ◽

Learning Techniques ◽

Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

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