Improving diabetes disease patients classification using stacking ensemble method with PIMA and local healthcare data

Diabetes mellitus, a chronic metabolic disorder, continues to be a major public health issue around the world. It is estimated that one in every two diabetics is undiagnosed. Early diagnosis and management of diabetes can also prevent or delay the onset of complications. With the help of a variety of machine learning and deep learning models, stacking algorithms, and other techniques, our study's goal is to detect diseases early. In this study, we propose two stacking-based models for diabetes disease classification using a combination of the PIMA Indian diabetes dataset, simulated data, and additional data collected from a local healthcare facility. We use both the classical and deep neural network stacking ensemble methods to combine the predictions of multiple classification models and improve classification accuracy and robustness. In the evaluation protocol, we used both the train-test and cross-validation (CV) techniques to validate our proposed model. The highest accuracy is obtained by stacking ensemble with three NN architectures, resulting in an accuracy of 95.50 %, precision of 94 %, recall of 97 %, and f1-score of 96 % using 5-fold CV on simulation study. The stacked accuracy obtained from ML algorithms for the Pima Indian Diabetes dataset is 75.03 % using the train-test split protocol, while the accuracy obtained from the CV protocol is 77.10 % on the stacked model. The range of performance scores that outperformed the CV protocol 2.23 %–12 %. Our proposed method achieves a high accuracy range from 92 % to 95 %, precision, recall, and F1-score ranges from 88 % to 96 % using classical and deep neural network (NN)-based stacking method on the primary dataset. The proposed dataset and ensemble method could be useful in the early detection and treatment of diabetes, as well as in the advancement of machine learning and data analysis techniques in the healthcare industry

Prediction of chronic liver disease patients using integrated projection based statistical feature extraction with machine learning algorithms

The healthy liver plays more than 500 organic roles in the human body, while a malfunction may be dangerous or even deadly. Early diagnosis and treatment of liver disease can improve the likelihood of survival. Machine learning (ML) is a powerful tool that can assist healthcare professionals during the diagnostic process for a hepatic patient. The standard ML system includes the methods of data pre-processing, feature extraction, and classification. In the feature extraction stage, ML researchers frequently use projection-based feature extraction approaches to remove data redundancy, but this does not produce the desired results. In addition, most statistical projection methods have different purposes when projecting original features. The Indian liver patient dataset (ILPD) from the University of California, Irvin (UCI) repository is used in this study to classify chronic liver disease. The data set has 583 patient disease records; 416 patients have liver disease, and 167 do not. Using several projection methods, we proposed an integrated feature extraction approach to categorize liver patients. In the pipeline, the proposed method first imputes the missing values and outliers for pre-treatment. Then, integrated feature extraction applies the pre-processed data to extract the significant features for classification. A simulation study is also being conducted to strengthen the suggested methodology. The proposed approach incorporates several ML algorithms, including logistic regression (LR), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), and the ensemble voting classifier. The offered system has an accuracy of 88.10%, a precision of 85.33%, a recall of 92.30%, an F1 score of 88.68%, and an AUC score of 88.20% in predicting liver diseases. Our proposed technique yielded 0.10–18.5% better results than the latest existing studies. The findings suggest that the recommended system could be used to supplement a physician's diagnosis of liver disease

Unlocking stroke prediction: Harnessing projection-based statistical feature extraction with ML algorithms

Non-communicable diseases, such as cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for approximately 71% of all deaths worldwide. Stroke, a cerebrovascular disorder, is one of the leading contributors to this burden among the top three causes of death. Early recognition of symptoms can encourage a balanced lifestyle and provide essential information for stroke prediction. To identify a stroke patient and risk factors, machine learning (ML) is a key tool for physicians. Due to different data measurement scales and their probability distributional assumptions, ML-based algorithms struggle to detect risk factors. Furthermore, when dealing with risk factors with high-dimensional features, learning algorithms struggle with complexity. In this study, rigorous statistical tests are used to identify risk factors, and PCA-FA (Integration of Principal Components and Factors) and FPCA (Factor Based PCA) approaches are proposed for projecting suitable feature representations for improving learning algorithm performances. The study dataset consists of different clinical, lifestyle, and genetic attributes, allowing for a comprehensive analysis of potential risk factors associated with stroke, which contains 5110 patient records. Using significant test (P-value <0.05), chi-square and independent sample t-test identified age, heart_disease, hypertension, work_type, ever_married, bmi, and smoking_status as risk factors for stroke. To develop the predicting model with proposed feature extraction techniques, random forests approach provides the best results when utilizing the PCA-FA method. The best accuracy rate for this approach is 92.55%, while the AUC score is 98.15%. The prediction accuracy has increased from 2.19% to 19.03% compared to the existing work. Additionally, the prediction results is robustified and reproducible with a stacking ensemble-based classification algorithm. We also developed a web-based application to help doctors diagnose stroke risk based on the findings of this study, which could be used as an additional tool to help doctors diagnose

Efficacy of a dementia intensive support (DIS) service at preventing admissions to medical and psychiatric wards: qualitative and quantitative evaluation.

AIMS AND METHOD: To establish whether a dementia intensive support (DIS) service that is part of a crisis resolution and home treatment team for older people is preventing admissions to acute hospital and psychiatric wards. The number of referrals in 2017 to the DIS service was established and those admitted to hospital ascertained. Senior doctors examined 30 sets of notes in detail and reached a conclusion on whether DIS had contributed to admission prevention. This information was then re-examined in two meetings with at least eight senior psychiatrists present. A consensus opinion was then reached as to whether DIS had contributed to admission prevention in each case. RESULTS: Over 12 months, 30/171 patients (18%) referred were admitted to hospital. For the subset of 30 referrals examined in detail, DIS contributed to admission avoidance in 21 cases (70%). CLINICAL IMPLICATIONS: Our evaluation demonstrates that the DIS service is an effective way of preventing admission