Early and accurate diagnosis of Alzheimer Disease is critical for effective clinical intervention, particularly in distinguishing it from Mild Cognitive Impairment, a prodromal stage marked by subtle structural changes. In this study, we propose a hybrid deep learning ensemble framework for Alzheimer Disease classification using structural magnetic resonance imaging. Gray and white matter slices are used as inputs to three pretrained convolutional neural networks such as ResNet50, NASNet, and MobileNet, each fine tuned through an end to end process. To further enhance performance, we incorporate a stacked ensemble learning strategy with a meta learner and weighted averaging to optimally combine the base models. Evaluated on the Alzheimer Disease Neuroimaging Initiative dataset, the proposed method achieves state of the art accuracy of 99.21% for Alzheimer Disease vs. Mild Cognitive Impairment and 91.0% for Mild Cognitive Impairment vs. Normal Controls, outperforming conventional transfer learning and baseline ensemble methods. To improve interpretability in image based diagnostics, we integrate Explainable AI techniques by Gradient weighted Class Activation, which generates heatmaps and attribution maps that highlight critical regions in gray and white matter slices, revealing structural biomarkers that influence model decisions. These results highlight the frameworks potential for robust and scalable clinical decision support in neurodegenerative disease diagnostics.

Alzheimers Disease (AD) is a progressive neurodegenerative disorder that poses significant challenges in its early diagnosis, often leading to delayed treatment and poorer outcomes for patients. Traditional diagnostic methods, typically reliant on single data modalities, fall short of capturing the multifaceted nature of the disease. In this paper, we propose a novel multimodal framework for the early detection of AD that integrates data from three primary sources: MRI imaging, cognitive assessments, and biomarkers. This framework employs Convolutional Neural Networks (CNN) for analyzing MRI images and Long Short-Term Memory (LSTM) networks for processing cognitive and biomarker data. The system enhances diagnostic accuracy and reliability by aggregating results from these distinct modalities using advanced techniques like weighted averaging, even in incomplete data. The multimodal approach not only improves the robustness of the detection process but also enables the identification of AD at its earliest stages, offering a significant advantage over conventional methods. The integration of biomarkers and cognitive tests is particularly crucial, as these can detect Alzheimer's long before the onset of clinical symptoms, thereby facilitating earlier intervention and potentially altering the course of the disease. This research demonstrates that the proposed framework has the potential to revolutionize the early detection of AD, paving the way for more timely and effective treatments

These were five major works and twelve other works and thus included diverse views of integrating AI in cardiovascular treatment. Synthesis of Results The data obtained was then combined to provide an integrated view on the effect of early detection by AI in the context of CVDs on health care costs and patients. The synthesis was to compare the mostly used diagnosing techniques with the newer AI techniques; the merits and demerits of integration of AI . Ethical Considerations Each of the studies considered within this systematic review complied with ethical procedures applicable for investigation involving human participants. Issues of privacy and security were also discussed particularly where patients' data were involved.

Metabolic syndrome (MetS) is a medication condition characterized by abdominal obesity, insulin resistance, hypertension and hyperlipidemia. It increases the risk of majority of chronic diseases, including type 2 diabetes mellitus, and affects about one quarter of the global population. Therefore, early detection and timely intervention for MetS are crucial. Standard diagnosis for MetS components requires blood tests conducted within medical institutions. However, it is frequently underestimated, leading to unmet need for care for MetS population. This study aims to use the least physiological data and free texts about exercises related activities, which are obtained easily in daily life, to diagnosis MetS. We collected the data from 40 volunteers in a nursing home and used data augmentation to reduce the imbalance. We propose a deep learning framework for classifying MetS that integrates natural language processing (NLP) and exercise monitoring. The results showed that the best model reported a high positive result (AUROC=0.806 and REC=76.3%) through 3-fold cross-validation. Feature importance analysis revealed that text and minimum heart rate on a daily basis contribute the most in the classification of MetS. This study demonstrates the potential application of data that are easily measurable in daily life for the early diagnosis of MetS, which could contribute to reducing the cost of screening and management for MetS population.

It is acknowledged that the most common cause of dementia worldwide is Alzheimer's disease (AD). This condition progresses in severity from mild to severe and interferes with people's everyday routines. Early diagnosis plays a critical role in patient care and clinical trials. Convolutional neural networks (CNN) are used to create a framework for identifying specific disease features from MRI scans Classification of dementia involves approaches such as medical history review, neuropsychological tests, and magnetic resonance imaging (MRI). However, the image dataset obtained from Kaggle faces a significant issue of class imbalance, which requires equal distribution of samples from each class to address. In this article, to address this imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is utilized. Furthermore, a pre-trained convolutional neural network has been applied to the DEMNET dementia network to extract key features from AD images. The proposed model achieved an impressive accuracy of 98.67%.

In the field of Alzheimer's disease diagnosis, segmentation and classification tasks are inherently interconnected. Sharing knowledge between models for these tasks can significantly improve training efficiency, particularly when training data is scarce. However, traditional knowledge distillation techniques often struggle to bridge the gap between segmentation and classification due to the distinct nature of tasks and different model architectures. To address this challenge, we propose a dual-stream pipeline that facilitates cross-task and cross-architecture knowledge sharing. Our approach introduces a dual-stream embedding module that unifies feature representations from segmentation and classification models, enabling dimensional integration of these features to guide the classification model. We validated our method on multiple 3D datasets for Alzheimer's disease diagnosis, demonstrating significant improvements in classification performance, especially on small datasets. Furthermore, we extended our pipeline with a residual temporal attention mechanism for early diagnosis, utilizing images taken before the atrophy of patients' brain mass. This advancement shows promise in enabling diagnosis approximately six months earlier in mild and asymptomatic stages, offering critical time for intervention.

Parkinson's disease is a widespread neurodegenerative condition necessitating early diagnosis for effective intervention. This paper introduces an innovative method for diagnosing Parkinson's disease through the analysis of human EEG signals, employing a Support Vector Machine (SVM) classification model. this research presents novel contributions to enhance diagnostic accuracy and reliability. Our approach incorporates a comprehensive review of EEG signal analysis techniques and machine learning methods. Drawing from recent studies, we have engineered an advanced SVM-based model optimized for Parkinson's disease diagnosis. Utilizing cutting-edge feature engineering, extensive hyperparameter tuning, and kernel selection, our method achieves not only heightened diagnostic accuracy but also emphasizes model interpretability, catering to both clinicians and researchers. Moreover, ethical concerns in healthcare machine learning, such as data privacy and biases, are conscientiously addressed. We assess our method's performance through experiments on a diverse dataset comprising EEG recordings from Parkinson's disease patients and healthy controls, demonstrating significantly improved diagnostic accuracy compared to conventional techniques. In conclusion, this paper introduces an innovative SVM-based approach for diagnosing Parkinson's disease from human EEG signals. Building upon the IEEE framework and previous research, its novelty lies in the capacity to enhance diagnostic accuracy while upholding interpretability and ethical considerations for practical healthcare applications. These advances promise to revolutionize early Parkinson's disease detection and management, ultimately contributing to enhanced patient outcomes and quality of life.

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. With the help of machine learning algorithms and mechanistic modeling approaches, such as agent-based modeling, an in-depth analysis of the simulation of cell dynamics is possible as well as of high-dimensional omics resources indicative of pathway signaling changes. Here, we give a background on advances in research on brain-immune system cross-talk in Alzheimer's disease and review recent machine learning and mechanistic modeling approaches which leverage modern omics technologies for blood-based immune system-related biomarker discovery.

We introduce Walk4Me, a telehealth community mobility assessment system designed to facilitate early diagnosis, severity, and progression identification. Our system achieves this by 1) enabling early diagnosis, 2) identifying early indicators of clinical severity, and 3) quantifying and tracking the progression of the disease across the ambulatory phase of the disease. To accomplish this, we employ an Artificial Intelligence (AI)-based detection of gait characteristics in patients and typically developing peers. Our system remotely and in real-time collects data from device sensors (e.g., acceleration from a mobile device, etc.) using our novel Walk4Me API. Our web application extracts temporal/spatial gait characteristics and raw data signal characteristics and then employs traditional machine learning and deep learning techniques to identify patterns that can 1) identify patients with gait disturbances associated with disease, 2) describe the degree of mobility limitation, and 3) identify characteristics that change over time with disease progression. We have identified several machine learning techniques that differentiate between patients and typically-developing subjects with 100% accuracy across the age range studied, and we have also identified corresponding temporal/spatial gait characteristics associated with each group. Our work demonstrates the potential of utilizing the latest advances in mobile device and machine learning technology to measure clinical outcomes regardless of the point of care, inform early clinical diagnosis and treatment decision-making, and monitor disease progression.

"With the successful execution of our model, we predicted the association of highly significant cardiovascular disease genes tied to demographic variables like race, gender and age." said Zeeshan Ahmed, a core faculty member at the Rutgers Institute for Health, Health Care Policy and Aging Research (IFH) and lead author of the study, published in Genomics. According to the World Health Organization, cardiovascular disease is the leading cause of death globally, yet it is estimated that more than 75 percent of premature cardiovascular disease is preventable. Atrial fibrillation and heart failure contribute to about 45 percent of all cardiovascular disease deaths. Despite significant advancements in cardiovascular disease diagnostics, prevention and treatment, about half of the affected patients reportedly die within five years of receiving a diagnosis because of a variety of reasons. Researchers said the use of AI and machine learning can accelerate our ability to identify genes that have important implications for cardiovascular disease, which can lead to improvements in diagnoses and treatment.