Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

/pdf/efficient-prediction-of-cardiovascular-disease-using-machine-1g3efyu5h4.pdf

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25% accuracy improvement by tuning the hyperparameter ‘k’ to 9 with the chi-squared attribute set.

Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators

Mostly in the agriculture sector, identifying rotten fruits has been critical. The classification of fresh and rotting fruits is typically carried out by humans, which is ineffective for fruit growers. Humans wear out by doing the same role many days, but robots do not. As a result, the study proposed a method for reducing human effort, lowering production costs, and shortening production time by detecting defects in agricultural fruits. If the defects are not detected, the contaminated fruits can contaminate the good fruits. As a result, we proposed a model to prevent the propagation of rottenness. From the input fruit images, the proposed model classifies the fresh and rotting fruits. We utilized three different varieties of fruits in this project: apple, banana, and oranges. The features from input fruit images are collected using a Convolutional Neural Network, and the images are categorized using Max pooling, Average pooling, and MobileNetV2 architecture. The proposed model's performance is tested on a Kaggle dataset, and it achieves the highest accuracy in training data is 99.46% and in the validation set is 99.61% by applying MobileNetV2. The Max pooling achieved 94.49% training accuracy and validation accuracy is 94.97%. Besides, the Average pooling achieved 93.06% training accuracy and validation accuracy is 93.72%. The findings revealed that the proposed CNN model is capable of distinguishing between fresh and rotting fruits.

Implementation of Deep Learning Methods to Identify Rotten Fruits

The COVID-19 coronavirus pandemic is wreaking havoc on the world's health. The healthcare sector is in a state of disaster. Many precautionary steps have been taken to prevent the spread of this disease, including the usage of a mask, which is strongly recommended by the World Health Organization (WHO). In this paper, we used three deep learning methods for face mask detection, including Max pooling, Average pooling, and MobileNetV2 architecture, and showed the methods detection accuracy. A dataset containing 1845 images from various sources and 120 co-author pictures taken with a webcam and a mobile phone camera is used to train a deep learning architecture. The Max pooling achieved 96.49% training accuracy and validation accuracy is 98.67%. Besides, the Average pooling achieved 95.190/0 training accuracy and validation accuracy is 96.23%. MobileNetV2 architecture gained the highest accuracy 99.72% for training and 99.82 % for validation.

Face Mask Detection using Convolutional Neural Network (CNN) to reduce the spread of Covid-19

Predicting cardiac disease is considered one of the most challenging tasks in the medical field. It takes a lot of time and effort to figure out what’s causing this, especially for doctors and other medical experts. In this paper, various Machine Learning algorithms such as LR, KNN, SVM, and GBC, together with the GridSearchCV, predict cardiac disease. The system uses a 5-fold cross-validation technique for verification. A comparative study is given for these four methodologies. The Datasets for both Cleveland, Hungary, Switzerland, and Long Beach V and UCI Kaggle are used to analyze the models’ performance. It is found in the analysis that the Extreme Gradient Boosting Classifier with GridSearchCV gives the highest and nearly comparable testing and training accuracies as 100% and 99.03% for both the datasets (Hungary, Switzerland &; Long Beach V and UCI Kaggle). Moreover, it is found in the analysis that XGBoost Classifier without GridSearchCV gives the highest and nearly comparable testing and training accuracies as 98.05% and 100% for both the datasets (Hungary, Switzerland & Long Beach V and UCI Kaggle). Furthermore, the analytical results of the proposed technique are compared with previous heart disease prediction studies. It is evident that amongst the proposed approach, the Extreme Gradient Boosting Classifier with GridSearchCV is producing the best hyperparameter for testing accuracy. The primary aim of this paper is to develop a unique model-creation technique for solving real-world problems. 

/pdf/efficient-medical-diagnosis-of-human-heart-diseases-using-129gugg6.pdf

Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV

Chronic Kidney disease (CKD), a slow and late-diagnosed disease, is one of the most important problems of mortality rate in the medical sector nowadays Based on this critical issue, a significant number of men and women are now suffering due to the lack of early screening systems and appropriate care each year However, patients’ lives can be saved with the fast detection of disease in the earliest stage In addition, the evaluation process of machine learning algorithm can detect the stage of this deadly disease much quicker with a reliable dataset In this paper, the overall study has been implemented based on four reliable approaches, such as Support Vector Machine (henceforth SVM), AdaBoost (henceforth AB), Linear Discriminant Analysis (henceforth LDA), and Gradient Boosting (henceforth GB) to get highly accurate results of prediction These algorithms are implemented on an online dataset of UCI machine learning repository The highest predictable accuracy is obtained from Gradient Boosting (GB) Classifiers which is about to 9980% accuracy Later, different performance evaluation metrics have also been displayed to show appropriate outcomes To end with, the most efficient and optimized algorithms for the proposed job can be selected depending on these benchmarks

Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm

The chronic kidney disease is the loss of kidney function. Often time, the symptoms of the disease is not noticeable and a significant amount of lives are lost annually due to the disease. Using machine learning algorithm for medical studies, the disease can be predicted with a high accuracy rate and a very short time. Using four of the supervised classification learning algorithms, i.e., logistic regression, Decision tree, Random Forest and KNN algorithms, the prediction of the disease can be done. In the paper, the performance of the predictions of the algorithms are analyzed using a pre-processed dataset. The performance analysis is done base on the accuracy of the results, prediction time, ROC and AUC Curve and error rate. The comparison of the algorithms will suggest which algorithm is best fit for predicting the chronic kidney disease.

Implementation of Machine Learning Algorithms to Detect the Prognosis Rate of Kidney Disease

The analysis of different sports data to get valuable insight has become immensely important nowadays. Profuse application of Artificial Intelligence in different sectors has become a very popular trend as well. However, the application of AI in sports analytics is still a new research domain left for exploration. With a view to applying AI in sports analytics, we have deployed Inception V3 and MobileNet which are Google’s most popular Convolutional Neural Networks to successfully recognize five different sports events from a huge image dataset of these events. Both of the models have achieved a very high performance in terms of accuracy, precision, recall, and f-measure while applied to the target dataset for successful classification.

Olympic Sports Events Classification Using Convolutional Neural Networks

Predicting the pregnancy termination and controlling the child mortality rate has always been a great challenge for third world country. This research targets to extract out best subset of features to predict pregnancy termination more accurately relative to previous researches. To facilitate this noble purpose, we have carried out an extensive research on Bangladesh Demographic and Health Survey (BDHS) 2014, that find out the most contributing attributes of pregnancy termination in Bangladesh. Bivariate and multivariate analyses on this data shows interesting details to find out the recent causes for pregnancy termination. However, for finding out the intended features first demographically feature selection performed with Weka provided visualization tools and secondly Weka provided feature ranking attribute evaluators such as Correlation, Gain Ratio, One R, Symmetrical Uncertainty, Information Gain, Relief are used. After minimizing the subset of features, we apply three traditional machine learning classifiers (Naive Byes, Bayesian Network, Decision Stump) along with the hybrid method which shows better performance in terms of performance metrics. This research improved accuracy 10.238% for Naive Byes, 8.2657% for Bayesian Network, 3.5853% for Decision Stump and 9.03% for Hybrid.

Shahana Shultana

Papers

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm

Implementation of Machine Learning Algorithms to Detect the Prognosis Rate of Kidney Disease

Olympic Sports Events Classification Using Convolutional Neural Networks

Minimizing the Subset of Features on BDHS Dataset to Improve Prediction on Pregnancy Termination