Disease Prediction Research Articles

Hybrid approach for Recurrence of Cardiovascular Disease Prediction Using Machine Learning Techniques

Cardiovascular disease remains a global health concern, being a leading cause of mortality. In response to the critical nature of heart-related conditions, this research focuses on the development of smart systems leveraging machine learning algorithms for accurate and timely diagnosis. The study explores various machine learning approaches to predict recurrence of heart diseases based on patient data encompassing key health factors. With heart disease being a prevalent cause of death worldwide, real-time forecasting methods from medical data sources have become crucial. The implementation of machine learning in healthcare demonstrates its potential for early and precise recurrence of disease detection. Despite the abundance of health information generated by medical institutions, there is an underutilization of this data, leading to a healthcare system that is "data rich" but "knowledge poor." This work aims to address this gap by presenting a reliable recurrence heart disease prediction system. The research highlights the necessity for effective analysis methods to uncover connections and patterns within healthcare data. The proposed prediction system utilizes a diverse set of health factors, contributing to a comprehensive understanding of heart disease. By leveraging machine learning algorithms, the system aims to enhance the accuracy and efficiency of diagnosis. The findings of this research not only contribute to the advancement of predictive healthcare but also underscore the significance of unlocking valuable insights from the vast pool of medical data. Ultimately, the implementation of such intelligent systems holds promise for improving patient outcomes and reducing the burden of cardiovascular diseases on global healthcare systems. The paper demonstrated Hybrid methods: Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB), to build the prediction models. Data preprocessing and feature selection steps were done before building the models. The models were evaluated based on the accuracy, precision, recall, and F1-score. The Random Forest model performed best with 94.27% accuracy.

Accuracy of MRI Versus PET/CT in the Prediction of Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer.

Background Breast-conserving surgeries have significantly advanced breast cancer treatment, offering favorable oncological outcomes, enhanced cosmetic results, reduced postoperative morbidity, and better psychological acceptance compared to mastectomy. The introduction of neoadjuvant therapy has expanded the applicability of breast conservation surgery to include locally advanced tumors. Tumor response to neoadjuvant chemotherapy is evaluated using imaging modalities such as breast ultrasound, breast magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT). Accurate prediction of therapeutic response facilitates the planning of surgical and adjuvant treatments. This study aims to compare the diagnostic accuracy of MRI and PET/CT in predicting treatment response to neoadjuvant chemotherapy in breast cancer patients. Methods This retrospective study was conducted at a tertiary care center in Bahrain. A total of 138 patients with locally advanced breast cancer or human epidermal growth factor receptor-2 (HER2) positive, hormone receptor-negative cancers who underwent breast-conserving surgeries between June 2018 and December 2022 were included. The inclusion criteria focused on patients achieving a complete pathological responsefollowing neoadjuvant systemic therapy, ensuring a homogenous study population. Patients with hormone receptor-positive early breast cancers or metastatic tumors, ineligible for neoadjuvant chemotherapy, were excluded. Non-responders and partial responders were also excluded from the study. Statistical analysis was performed using IBM SPSS v26.0 (IBM Corp., Armonk, US). Response rates for the imaging modalities and histopathology results were assessed. Agreement between histology and imaging modalities was computed using kappa statistics. Diagnostic performance for predicting "no residual" disease was evaluated using the McNemar Test. All tests were two-tailed, with a p-value <0.05 considered statistically significant. Results The study included 138 patients, of whom 73 (52.9%) had an incomplete response or residual disease, while 65 (47.1%) had a complete response or no residual disease according to histology reports. There was slight agreement between post-neoadjuvant MRI and histology results (Cohen's kappa 0.172, p=0.010), while substantial agreement was observed between post-neoadjuvant PET/CT and histology results (Cohen's kappa 0.614, p=0.000). PET/CT demonstrated a higher sensitivity of 93.8% (p<0.001) and a specificity of 68.5%. Although MRI was more specific, the positive predictive value was comparable for both PET/CT and MRI. Conclusion PET/CT shows higher sensitivity and can serve as an early marker for predicting complete pathological response in post-neoadjuvant breast cancer patients. However, the prediction of residual disease is optimized by combining both MRI and PET/CT as diagnostic modalities.

CARDIOVASCULAR DISEASE PREDICTION USING RANDOM FOREST

Heart plays a vital role in living organisms. Heart disease, which is also known as coronary artery disease, is a leading cause of death globally, exacerbated by various external factors. Nowadays everything has become so simple with the upcoming technologies like machine learning. Machine learning provides a promising solution to this crisis. Machine learning, a subset of artificial intelligence, involves algorithms that help in predicting outcomes and making decisions based on large amounts of data. It has become a buzzword in almost every sector or industry due to its transformative potential. In this study, we employ the Random Forest algorithm to predict heart disease. This model achieved an accuracy of 90.16%, highlighting its potential in accurately predicting heart health status. Although there are many machine learning models available for heart disease prediction, we chose Random Forest for its robustness and reliability.

Enhanced heart disease prediction through hybrid CNN-TLBO-GA optimization: a comparative study with conventional CNN and optimized CNN using FPO algorithm

Enhanced heart disease prediction through hybrid CNN-TLBO-GA optimization: a comparative study with conventional CNN and optimized CNN using FPO algorithm

An MLP-CNN Model for Real-time Health Monitoring and Intervention

The use of Artificial Intelligence (AI) in healthcare, particularly in real-time health monitoring and predictive interventions for chronic diseases, has many benefits but also many drawbacks. Existing health risk prediction algorithms face accuracy issues and, due to the wide variety of health profiles, general algorithm applicability is problematic. The proposed model solves this issue by using an advanced AI framework to improve the accuracy of the prediction of Chronic Kidney Disease (CKD) and eliminate false positives. Our hybrid Deep Learning (DL) method blends a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN), thus including real-time feedback to help the system learn from its predictions and results. The proposed system solves a major problem in this area and sets a new benchmark for AI applications in healthcare by directly addressing prediction accuracy. It offers more tailored, accurate, and responsive chronic disease management, improving patient outcomes and healthcare resource efficiency.

Prediction of heart disease using a novel slap swarm optimized multi-objective random forest

Heart disease is a leading cause of death worldwide, becoming a major health concern for many people. Among the most important tasks and regular diagnostic research entails the identified cardiac issues, like heart diseases, valve conditions, etc. Early detection of heart disease may save many lives. The application of machine learning techniques in the medical sector has advanced significantly. A novel Slap Swarm Optimized Multi-Objective Random Forest (SSO-MORF) approach was presented in the proposed work for predicting cardiac disease. For this suggested investigation, heart disease information using individual customer identification at the University of California Irvine (UCI) was utilized, and data mining methods like categorization were applied. Min–max normalization and principal component analysis (PCA) were used for data preparation and feature extraction. As a result, a rather basic supervised machine learning technique may be used to predict heart disease quite accurately and with excellent potential value. According to this study, a classification based on the RF technique produced results with 99.45% accuracy, 98.3% recall, 98.6% precision, and a 98.9% f1 score. This finding demonstrates that our system is more efficient at predicting cardiac disease than other cutting-edge techniques.

Advanced skin disease detection: Image processing and modified genetic optimization with supervised k-nearest neighbors

The use of modern technology to improve skin disease diagnosis includes things like artificial intelligence (AL), deep learning (DL) and computer vision. Medical images of skin lesions, dermatoscopic images, or thermal imaging can be analyzed by automated algorithms and image processing techniques to diagnose skin diseases. Diseases can cause physical and emotional suffering, making them silent killers. Extreme instances might cause skin cancer. skin disease diagnosis from clinical photographs is a key medical image processing challenge. Manual skin diagnosis takes time and it is subjective for doctors. Computerized skin disease prediction can simplify patient and dermatologist treatment planning. To tackle these issues, we proposed a genetic optimization and supervised K-Nearest Neighbor (MGO-SKNN) technique for detecting advanced skin diseases. The research gathers clinical data for detecting advanced skin diseases. Three methods were used to preprocess our data: noise reduction, hair removal and image resizing. The Grey Level Co-Occurrence Matrix (GLCM) approach was used in this work to extract features. The integration of image processing into MGO-SKNN represents a significant step forward in the quest for accurate, efficient and cutting-edge skin disease diagnosis. The metrics like accuracy (97.53%), precision (95.20%), recall (96.74%) and f1-score (98.06%) of our suggested technique, MGO-SKNN, exceed the traditional approaches for skin detection.

Advances in Cerebral Palsy Treatment

Background: Cerebral palsy is a complex neurodevelopmental disorder with various etiological factors and treatment options. This narrative review aimed to summarize the causes of cerebral palsy, identify areas needing additional research in treatment approaches, and highlight areas requiring further investigation. In order to provide a thorough overview of management techniques to lessen the effects of the illness and its consequences, this review has drawn data from a number of studies. Introduction: Prematurity increases the risk of brain damage during the developing stage and accounts for a sizable fraction of cerebral palsy cases. In a sizable portion of cases, maternal diabetes and hypertension are listed as the main causes. Damage to the brain tissue results from hypoxic-ischemic injuries sustained during pregnancy that upset the equilibrium of oxidants and antioxidants. To alter the oxidative stress pathway and ease related issues, pharmacological treatments, such as therapeutic hypothermia, free radical inhibition therapy, and mitochondrial therapy, have been proposed. Therapeutic strategies, such as physiotherapy, occupational therapy, speech therapy, and surgical interventions, have added quality to the lives of the children. Some of the most recent developments in this area include the development of biomarkers for muscle activity detection, machine learning to predict the types of cerebral palsy and abnormal movements, disease prediction with eye images, wireless inertia measuring unit for spasticity detection, computerbased video analysis of typical and atypical infants, identification of intellectual disabilities with algorithms, and deep learning methods for predicting cerebral palsy. Methods: This narrative review is based on a careful analysis of numerous researches conducted on cerebral palsy, which have served as the basis for statistical distribution. It reviews the causes of cerebral palsy, available treatments, and ongoing research with the goal of providing physicians and researchers in the field with useful information. The objectives, study questions, inclusion criteria, and search approach have all been outlined in a thorough protocol. To find pertinent research published up to September 2021, a literature search was carried out using electronic databases, including Google Scholar, PubMed, Cochrane Library, Scopus, and Web of Science. A combination of pertinent keywords, such as "cerebral palsy," "management," "technology," "wearable technology," "prematurity," and "artificial intelligence," has been used in the search approach. Results: Recent advances in the field include the discovery of biomarkers for the detection of muscle activity, machine learning algorithms to predict the types of cerebral palsy and abnormal movements, disease prediction using eye images, wireless inertia measuring units for the detection of spasticity, computer-based video analysis for the detection of atypical infants, and algorithms to identify intellectual disabilities. Additionally, employing technologies, like virtual reality systems, electrical stimulators, activity trackers, machine learning, and deep learning approaches, has shown promise in evaluating, diagnosing, and predicting treatment outcomes linked to gait, upper limb, and lower limb function. Conclusion: Future research should examine the clinical application of nanomedicine, stem cell therapy, and cutting-edge therapeutic strategies to prevent hypoxic-ischemic damage in the developing brain. Additionally, research is required to effectively assist children with severe speech difficulties using alternate communication modalities and cutting-edge computational tools. The outcomes for people with cerebral palsy can be improved by combining interdisciplinary efforts with cutting-edge technological interventions.

Predictive model for left main coronary artery or triple vessel disease in patients with chronic coronary syndromes.

Data about prediction of left main coronary artery disease (LMCAD)/three-vessel disease (TVD) in patients with chronic coronary syndromes (CCS) are lacking. This study aimed to develop a model for predicting patients at risk of LMCAD/TVD. This study used retrospective data from patients with CCS scheduled for invasive coronary angiography (ICA) and who were retrospectively recruited between January 2018 and December 2020. Predictors were obtained and analyzed by using logistic regression analysis, and generated the prediction score. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. The cut-off value and area under the curve (AUC) were analyzed by using the receiver operating characteristic (ROC) curve. We recruited 162 patients with CCS. There were 75 patients in the non-LMCAD/TVD and 87 patients in the LMCAD/TVD groups. After the multivariate analysis, new onset of heart failure (HF) or left ventricular systolic dysfunction (LVSD) and suspected CAD, ST elevation (STE) in aVR, STE in V1 and lateral ST depression (STD) were associated with increased risk of LMCAD/TVD. Based on these 4 predictors, the prediction score was created. The cut-off value of the prediction score by using ROC curve analysis was 3.0. The sensitivity, specificity, PPV, and NPV were 71.26%, 86.67%, 86.11%, and 72.22%, respectively, with an AUC of 0.855. The CCS patients with new onset of HF or LVSD and suspected CAD, STE in aVR, and STE in V1 and lateral STD were associated with increased risk of LMCAD/TVD. The novel prediction score could predict LMCAD/TVD in those patients with acceptable sensitivity, specificity, PPV, and NPV.

Cardiovascular disease risk factors prediction using deep learning convolutional neural networks

Heart disease remains a leading cause of mortality worldwide, prompting healthcare researchers to leverage analytical tools for comprehensive data analysis. This study focuses on exploring crucial parameters and employing deep learning (DL) techniques to enhance understanding and prediction of cardiovascular disease (CVD) risk factors. Utilizing SPSS and Weka tools, a cross-sectional and correlational design was employed to analyze extensive medical datasets. Binomial regression analysis revealed significant associations between age (𝑝 = 0.004) and body mass index (𝑝 = 0.002) with CVD development, highlighting their importance as risk factors. Leveraging Weka's DL algorithms, a predictive model was constructed to classify CVD causes. Particularly, convolutional neural networks (CNN) showcased remarkable accuracy, reaching 98.64%. The findings underscore the elevated risk of CVD among university students and employees in Saudi Arabia, emphasizing the need for heightened awareness and preventive measures, including dietary improvements and increased physical activity. This study underscores the importance of further research to enhance CVD risk perception among students and individuals in similar settings.

Machine Learning-Based Cardiovascular Disease Prediction System to reduce the incidence of occurrence

Health is a paramount factor when considering any nation's sustainable growth and development. The concentration of the larger population of Nigerians is in the rural areas where public health infrastructures are either inadequate or not available. This has affected the larger labor force, leaving society vulnerable to high health. In recent times, one of the major health challenges facing rural and urban dwellers is heart disease occasioned by the non-existence of proper medical diagnosis. This study develops a machine learning model to predict the onset of heart disease to minimize onset cases of occurrence. The health prediction system is integrated with a prediction module to help in providing public healthcare services to rural dwellers. The developed system was comparatively achieved using hybrid machine learning techniques, which include a support vector machine and random forest in the design of the predictive module. The system integrates rural dwellers' health records with the global health database to form part of the Big Data for global access and considerations. The performance evaluation was achieved with the help of Random Forest. The system developed shows an average accuracy of 98% using machine learning algorithms. It also provides a convenient means for rural dwellers to access public health and enables them to receive health predictions to guide against fatal heart disease.

Refining heart disease prediction accuracy using hybrid machine learning techniques with novel metaheuristic algorithms

Early diagnosis of heart disease is crucial, as it's one of the leading causes of death globally. Machine learning algorithms can be a powerful tool in achieving this goal. Therefore, this article aims to increase the accuracy of predicting heart disease using machine learning algorithms. Five classification models are explored: eXtreme Gradient Boosting (XGBC), Random Forest Classifier (RFC), Decision Tree Classifier (DTC), K-Nearest Neighbors Classifier (KNNC), and Logistic Regression Classifier (LRC). Additionally, four optimizers are evaluated: Slime mold Optimization Algorithm, Forest Optimization Algorithm, Pathfinder algorithm, and Giant Armadillo Optimization. To ensure robust model selection, a feature selection technique utilizing k-fold cross-validation is employed. This method identifies the most relevant features from the data, potentially improving model performance. The top three performing models are then coupled with the optimization algorithms to potentially enhance their generalizability and accuracy in predicting heart failure. In the final stage, the shortlisted models (XGBC, RFC, and DTC) were assessed using performance metrics like accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). This rigorous evaluation identified the XGGA hybrid model as the top performer, demonstrating its effectiveness in predicting heart failure. XGGA achieved impressive metrics, with an accuracy, precision, recall, and F1-score of 0.972 in the training phase, underscoring its robustness. Notably, the model's predictions deviated by less than 5.5 % for patients classified as alive and by less than 1.2 % for those classified as deceased compared to the actual outcomes, reflecting minimal error and high predictive reliability. In contrast, the DTC base model was the least effective, with an accuracy of 0.840 and a precision of 0.847. Overall, the optimization using the GAO algorithm significantly enhanced the performance of the models, highlighting the benefits of this approach.

Impact of peripheral lymphocyte subsets on prognosis for patients after acute ischemic stroke: A potential disease prediction model approach.

To investigate the relationship between peripheral blood lymphocyte subsets and prognosis in patients with acute ischemic stroke (AIS). We enrolled 294 patients with AIS and collected peripheral blood samples for analysis of lymphocyte subsets. Prognosis was assessed at 3 months using the modified Rankin Scale (mRS). Association between lymphocyte count and poor outcomes (mRS score >2) was assessed using logistic regression. Individualized prediction models were developed to predict poor outcomes. Patients in the mRS score ≤2 group had higher T-cell percentage (odds ratio [OR] = 0.947; 95% confidence interval [CI]: 0.899-0.998; p = 0.040), CD3+ T-cell count (OR = 0.999; 95% CI: 0.998-1.000; p = 0.018), and CD4+ T-cell count (OR = 0.998; 95% CI: 0.997-1.000; p = 0.030) than those in the mRS score >2 group 1-3 days after stroke. The prediction model for poor prognosis based on the CD4+ T-cell count showed good discrimination (area under the curve of 0.844), calibration (p > 0.05), and clinical utility. Lower T cell percentage, CD3+, and CD4+ T-cell counts 1-3 days after stroke were independently associated with increased risk of poor prognosis. Individualized predictive model of poor prognosis based on CD4+ T-cell count have good accuracy and may predict disease prognosis.

Genetic Algorithm-based Convolutional Neural Network Feature Engineering for Optimizing Coronary Heart Disease Prediction Performance.

This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection. Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model. We also employed information gain for feature selection optimization, transforming the CHD datasets into an image-like input for the CNN architecture. The efficacy of this method was benchmarked against traditional optimization strategies. The advanced GA-based CNN model outperformed traditional methods, achieving a substantial increase in accuracy. The optimized model delivered a promising accuracy range, with a peak of 85% in hyperparameter optimization and 100% accuracy when integrated with machine learning algorithms, namely naïve Bayes, support vector machine, decision tree, logistic regression, and random forest, for both binary and multiclass CHD prediction tasks. The integration of a GA into CNN feature engineering is a powerful technique for improving the accuracy of CHD predictions. This approach results in a high degree of predictive reliability and can significantly contribute to the field of AI-driven healthcare, with the possibility of clinical deployment for early CHD detection. Future work will focus on expanding the approach to encompass a wider set of CHD data and potential integration with wearable technology for continuous health monitoring.

Research on the application of statistical methods based on big data in the medical and health field

In the current era of information, the rise of big data technology has become a key force driving the development of the medical and health field. Statistical methods, as core tools for data analysis, have demonstrated unique advantages in processing vast medical datasets. This paper comprehensively analyzes the various applications of big data statistical methods in the medical and health field, focusing particularly on their practical effects and potential value in disease prediction, epidemiological research, medical resource optimization, and personalized medical services. Through a review of relevant literature and in-depth discussions of multiple cases, this paper reveals how statistical methods play a crucial role in improving diagnostic accuracy and medical service efficiency. It also addresses the challenges faced in actual applications, such as data privacy protection and technical standardization.

Predicting angiographic coronary artery disease using machine learning and high-frequency QRS

AimExercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the prediction of angiographic coronary artery disease by HFQRS analysis of cycling exercise ECG.Methods and resultsThis study prospectively included 140 inpatients and 59 healthy volunteers undergoing cycling exercise ECG. The CHD group (N=104) and non-CHD group (N=95) were determined by coronary angiography gold standard. Automated HF QRS analysis was performed by the blinded method. The coronary group was predominantly male, with a higher prevalence of age, BMI, hypertension, and diabetes than the non-coronary group (P<0.001\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P<0.001$$\\end{document}), higher lipid levels in the coronary group (P<0.005\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P<0.005$$\\end{document}), significantly longer QRS duration during exercise testing (P<0.005\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P<0.005$$\\end{document}), more positive leads (P<0.001\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P<0.001$$\\end{document}), and a greater proportion of significant changes in HFQRS (P<0.001\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P<0.001$$\\end{document}). Age, Gender, Hypertension, Diabetes, and HF QRS Conclusions were screened by correlation analysis and multifactorial retrospective analysis to construct the machine learning models of the XGBoost Classifier, Logistic Regression, LightGBM Classifier, RandomForest Classifier, Artificial Neural Network and Support Vector Machine, respectively.ConclusionMale, elderly, with hypertension, diabetes mellitus, and positive exercise stress test HFQRS conclusions suggested a high risk of CHD. The best performance of the Logistic Regression model was compared, and a column line graph for assessing the risk of CHD was further developed and validated.

THE DISCRIMINANT ANALYSIS FUNCTION WAS IMPLEMENTED TO PREDICT THE PRESENCE OF DIABETES

Diabetes is a condition blood sugar concentrations are high and there is something wrong with insulin inside the body. A hormone called insulin controls the equilibrium of blood sugar concentration in humans. Diabetes has high-risk health, such as CKD, CVD, skin disease or even blindness. The reason people suffer from diabetes is caused of bad consumption habits. Some symptoms of diabetes are frequent urination and feeling hungry too quickly. Diabetes is sometimes difficult to diagnose, which is why it is also referred to as the silent killer. A preventive way is an early prediction of diabetes disease. This is very important to do. In this study, the discriminant analysis algorithm is used along with machine learning techniques. In this study, machine learning techniques are used. Its name is discriminant analysis algorithm. Two popular versions are linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). This method is used because it is suitable for high-dimensional data and the discriminant analysis algorithm has minimal parameters. The discriminant analysis algorithm uses few parameters and this method appropriate for high-dimensional data. We'll compare the two approaches to find a way to demonstrate their dependability. Both approaches would be contrasted. Based on the result, QDA has the best performance. QDA can produce accuracy = 93.7%, TPR = 93.7%, precision = 94.3%, recall = 93.7% and F-measure = 93.9%. FPR of QDA is the lowest one, it is 1.02%. It means QDA has a small error in making predictions. Overall, based on the result QDA is the proven and proper method for detecting diabetes disease

The survey and discussion of research on heart disease prediction based on Apache Spark

Heart is the most important part of the human body, diseases that are related to heart cause a huge thread to human health. In this paper, methods that applied Apache Spark to heart disease related works would be shown and discussed in order to classify these methods and make a conclusion about the innovations and shortcomings of these works. These works are defined into two categories: the ones that adopted traditional machine learning method and the ones that used deep learning methods. By classifying these works into two types, commonalities and similar innovative approaches in the same category of methods can be better observed and summarized, facilitating a clearer comparison of the similarities and differences in the innovative focuses among similar yet distinct methods. By doing so, conclusions were made to show that apart from enhancing operational efficiency and reliability of models for diagnosing and treating heart diseases, current research utilizing Apache Spark in this field also identifies areas for improvement such as expanding sample data representation, speeding up data processing, and addressing concept drift issues with proposed solutions. By addressing these challenges, researchers aim to optimize existing methods using Apache Spark and advanced data analytics techniques to combat heart diseases.

Cardiovascular disease prediction and a visualization platform building through machine learning

In the increasing trend of population aging, medical security has become an important issue in social life that cannot be ignored. The elderly population generally faces the threat of a variety of cardiovascular diseases, which not only bring potential hazards to their physical health, but also pose more serious challenges to the medical system. Therefore, there is an urgent need to monitor the health indicators of the elderly and provide timely medical care. This article shows the corresponding machine learning model, which can provide relatively accurate predictions based on a given data set. By comparing and studying three Bayesian-optimized algorithms and baseline models (including Decision Tree, Support Vector Machine, Logical Regression, Random Forest, XGB, LGBM), a relatively better algorithm was selected. In this paper, it is believed that the model performance given by LGBM after Bayesian optimization is relatively good. It has a sound framework, high accuracy in predicting cardiovascular disease, and good performance when processing large-scale data, making it feasible for application in the field of medical security. At the same time, this research builds a visualization platform to afford assistance in the early detection of heart disease, is more friendly to non-professionals, and has made contributions in the fields of medical security and public health.

Forecasting alzheimer's disease in its early stages with the use of machine learning algorithms

When it comes to dementia in the elderly, Alzheimer's disease (AD) takes the cake. Metabolic disorders impact a huge portion of the global population; as a result, there is a growing interest in using machine learning to better understand these conditions. The annual increase in their incidence rates is quite concerning. Neurodegenerative alterations impact the brain in Alzheimer's disease. Diseases impacting memory and functioning will affect healthcare providers, patients, and families at an increasing rate as the population ages. The monetary, social, and economic spheres will all feel these impacts to a significant degree. It is difficult to foretell the onset of Alzheimer's disease in its early stages. Compared to treatments administered later in the disease's progression, those administered early in the disease's progression are more effective and have less mild side effects. Finding the optimal parameters for Alzheimer's disease prediction has involved the use of several techniques, including Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, and Voting classifiers. Parameters such as F1-score for ML models, Precision, Recall, and Accuracy are used to evaluate the performance of models that use the Open Access Series of Imaging Studies (OASIS) data to make predictions about Alzheimer's disease. Medical professionals can use the suggested categorization system to identify these illnesses. Reducing yearly mortality rates of Alzheimer's disease through early diagnosis using these ML algorithms is tremendously advantageous. When tested using AD test data, the suggested approach outperforms the competition with an average validation accuracy of more than 90%. In contrast to previous studies, this one achieved a far higher test accuracy score.