Heart Alert: A heart disease prediction system using machine learning approach and optimization techniques

Background@#Cardiovascular diseases belong to the top three leading causes of mortality in the Philippines with 17.8 % of the total deaths. Lifestyle-related habits such as alcohol consumption, smoking, poor diet and nutrition, high sedentary behavior, overweight, and obesity have been increasingly implicated in the high rates of heart disease among Filipinos leading to a significant burden to the country's healthcare system. The objective of this study was to predict the presence of heart disease using various machine learning algorithms (support vector machine, naïve Bayes, random forest, logistic regression, decision tree, and adaptive boosting) evaluated on an anonymized publicly available cardiovascular disease dataset. @*Methodology@#Various machine learning algorithms were applied on an anonymized publicly available cardiovascular dataset from a machine learning data repository (IEEE Dataport). A web-based application system named Heart Alert was developed based on the best machine learning model that would predict the risk of developing heart disease. An assessment of the effects of different optimization techniques as to the imputation methods (mean, median, mode, and multiple imputation by chained equations) and as to the feature selection method (recursive feature elimination) on the classification performance of the machine learning algorithms was made. All simulation experiments were implemented via Python 3.8 and its machine learning libraries (Scikit-learn, Keras, Tensorflow, Pandas, Matplotlib, Seaborn, NumPy). @*Results@#The support vector machine without imputation and feature selection obtained the highest performance metrics (90.2% accuracy, 87.7% sensitivity, 93.6% specificity, 94.9% precision, 91.2% F1-score and an area under the receiver operating characteristic curve of 0.902 ) and was used to implement the heart disease prediction system (Heart Alert). Following very closely were random forest with mean or median imputation and logistic regression with mode imputation, all having no feature selection which also performed well. @*Conclusion@#The performance of the best four machine learning models suggests that for this dataset, imputation technique for missing values may or may not be done. Likewise, recursive feature elimination for feature selection may not apply as all variables seem to be important in heart disease prediction. An early accurate diagnosis leading to prompt intervention efforts is very crucial as it improves the patient's quality of life and diminishes the risk of developing cardiac events.

Major depressive disorder prediction using data science

Background@#Major depressive disorder is a mood disorder that has affected many people worldwide. It is characterized by persistently low or depressed mood, anhedonia or decreased interest in pleasurable activities, feelings of guilt or worthlessness, lack of energy, poor concentration, appetite changes, psychomotor retardation or agitation, sleep disturbances, or suicidal thoughts. @*Objective@#The objective of the study was to predict the presence of major depressive disorder using a variety of machine learning classification algorithms (logistic regression, Naive Bayes, support vector machine, random forest, adaptive boosting, and extreme gradient boosting) on a publicly available depression dataset. @*Methodology@#After data pre-processing, several experiments were performed to assess the recursive feature elimination with cross validation as a feature selection method and synthetic minority over-sampling technique to address dataset imbalance. Several machine learning algorithms were applied on an anonymized publicly available depression dataset. Feature importance of the top performing models were also generated. All simulation experiments were implemented via Python 3.8 and its machine learning libraries (Scikit-learn, Keras, Tensorflow, Pandas, Matplotlib, Seaborn, NumPy). @*Results@#The top performing model was obtained by logistic regression with excellent performance metrics (91% accuracy, 93% sensitivity, 85% specificity, 93% recall, 93% F1-score, and 0.78 Matthews correlation coefficient). Feature importance scores of the most relevant attribute were also generated for the best model. @*Conclusion@#The findings suggest the utility of data science techniques powered by machine learning models to make a diagnosis of major depressive disorders with acceptable results. The potential deployment of these machine learning models in clinical practice can further enhance the diagnostic acumen of health professionals. Using data analytics and machine learning, data scientists can have a better understanding of mental health illness contributing to prompt and improved diagnosis thereby leading to the institution of early intervention and medical treatments ensuring the best quality of care for our patients.

Thyroid disorder classification using machine learning models

Introduction@#Thyroid hormones are produced by the thyroid gland and are essential for regulating the basal metabolic rate. Abnormalities in the levels of these hormones lead to two classes of thyroid diseases – hyperthyroidism and hypothyroidism. Detection and monitoring of these two general classes of thyroid diseases require accurate measurement and interpretation of thyroid function tests. The clinical utility of machine learning models to predict a class of thyroid disorders has not been fully elucidated. @*Objective@#The objective of this study is to develop machine learning models that classify the type of thyroid disorder on a publicly available thyroid disease dataset extracted from a machine learning data repository. @*Methods@#Several machine learning algorithms for classifying thyroid disorders were utilized after a series of pre-processing steps applied on the dataset. @*Results@#The best performing model was obtained by with XGBoost with a 99% accuracy and showing very good recall, precision, and F1-scores for each of the three thyroid classes. Generally, all models with the exception of Naïve Bayes did well in predicting the negative class generating over 90% in all metrics. For predicting hypothyroidism, XGBoost, decision tree and random forest obtained the most superior performance with metric values ranging from 96-100%. On the other end in predicting hyperthyroidism, all models have lower classification performance as compared to the negative and hypothyroid classes Needless to say, XGBoost and random forest did obtain good metric values ranging from 71-89% in predicting hyperthyroid class. @*Conclusion@#The findings of this study were encouraging and had generated useful insights in the application and development of faster automated models with high reliability which can be of use to clinicians in the assessment of thyroid diseases. The early and prompt clinical assessment coupled with the integration of these machine learning models in practice can be used to determine prompt and precise diagnosis and to formulate personalized treatment options to ensure the best quality of care to our patients.

Pre-trained convolutional neural networks in the assessment of bone scans for metastasis

Background@#Numerous applications of artificial intelligence have been applied in radiological imaging ranging from computer-aided diagnosis based on machine learning to deep learning using convolutional neural networks. One of the nuclear medicine imaging tests being commonly performed today is bone scan. The use of deep learning methods through convolutional neural networks in bone scintigrams has not been fully explored. Very few studies have been published on its diagnostic capability of convolutional neural networks in assessing osseous metastasis. @*Objective@#The aim of our study is to assess the classification performance of the pre-trained convolutional neural networks in the diagnosis of bone metastasis from whole body bone scintigrams of a local institutional dataset. @*Methods@#Bone scintigrams from all types of cancer were retrospectively reviewed during the period 2019-2020 at the University of Perpetual Help Medical Center in Las Pinas City, Metro Manila. The study was approved by the Institutional Ethical Review Board and Technical Review Board of the medical center. Bone scan studies should be mainly for metastasis screening. The pre-processing techniques consisting of image normalization, image augmentation, data shuffling, and train-test split (testing at 30% and the rest (70%) was split 85% for training and 15% for validation) were applied to image dataset. Three pre-trained architectures (ResNet50, VGG19, DenseNet121) were applied to the processed dataset. Performance metrics such as accuracy, recall (sensitivity), precision (positive predictive value), and F1-scores were obtained.@*Results@#A total of 570 bone scan images with dimension 220 x 646 pixel sizes in .tif file format were included in this study with 40% classified with bone metastasis while 60% were classified as without bone metastasis. DenseNet121 yielded the highest performance metrics with an accuracy rate of 83%, 76% recall, 86% precision, and 81% F1-score. ResNet50 and VGG19 had similar performance with each other across all metrics but generally lower predictive capability as compared to DenseNet121.@*Conclusion@#A bone metastasis machine learning classification study using three pre-trained convolutional neural networks was performed on a local medical center bone scan dataset via transfer learning. DenseNet121 generated the highest performance metrics with 83% accuracy, 76% recall, 86% precision and 81% F1-score. Our simulation experiments generated promising outcomes and potentially could lead to its deployment in the clinical practice of nuclear medicine physicians. The use of deep learning techniques through convolutional neural networks has the potential to improve diagnostic capability of nuclear medicine physicians using bone scans for the assessment of metastasis.