Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and is the most common cause of dementia. Early diagnosis of Alzheimer’s disease is critical for better management and treatment outcomes, but it remains a challenging task due to the complex nature of the disease. Clinical data, including a range of cognitive, functional, and demographic variables, play a crucial role in Alzheimer’s disease classification. Also, challenges such as data imbalance and high-dimensional feature sets often hinder model performance. Objective: This paper aims to propose a computationally efficient, reliable, and transparent machine learning-based framework for the classification of Alzheimer’s disease patients. This framework is interpretable and helps medical practitioners learn complex patterns in patients. Method: This study addresses these issues by employing boosting algorithms, for enhanced classification accuracy. To mitigate data imbalance, a random sampling technique is applied, ensuring a balanced representation of Alzheimer’s and healthy cases. Extensive feature analysis was conducted to identify the most impactful clinical features followed by feature reduction techniques to focus on the most informative clinical features, reducing model complexity and overfitting risks. Explainable AI tools, such as SHAP, LIME, ALE, and ELI5 are integrated to provide transparency into the model’s decision-making process, highlighting key features influencing the classification and allowing clinicians to understand and trust the key features driving the predictions. Results: This approach results in a robust, interpretable, and clinically relevant framework for Alzheimer’s disease diagnosis. The proposed approach achieved the best accuracy of 95%, demonstrating its effectiveness and potential for reliable early diagnosis of Alzheimer’s disease. Conclusions: This study demonstrates that integrating ensemble learning algorithms and explainable AI, while using a balanced dataset with feature selection, improves quantitative results and interpretability. This approach offers a promising method for early and better-informed clinical decisions.

Chronic kidney disease (CKD) is a significant global health challenge that requires timely detection and accurate prognosis for effective treatment and management. The application of machine learning (ML) algorithms for CKD detection and prediction holds promising potential for improving patient outcomes. By incorporating key features which contribute to CKD, these algorithms enhance our ability to identify high-risk individuals and initiate timely interventions. This research highlights the importance of leveraging machine learning techniques to augment existing medical knowledge and improve the identification and management of kidney disease. In this paper, we explore the utilization of diverse ML algorithms, including gradient boost (GB), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), histogram boost (HB), and XGBoost (XGB) to detect and predict chronic kidney disease (CKD). The aim is to improve early detection and prognosis, enhancing patient outcomes and reducing the burden on healthcare systems. We evaluated the performance of the ML algorithms using key metrics like accuracy, precision, recall, and F1 score. Additionally, we conducted feature significance analysis to identify the most influential characteristics in the detection and prediction of kidney disease. The dataset used for training and evaluation contained various clinical and demographic attributes of patients, including serum creatinine level, blood pressure, and age, among others. The proficiency analysis of the ML algorithms revealed consistent predictors across all models, with serum creatinine level, blood pressure, and age emerging as particularly effective in identifying individuals at risk of kidney disease. These findings align with established medical knowledge and emphasize the pivotal role of these attributes in early detection and prognosis. In conclusion, our study demonstrates the effectiveness of diverse machine learning algorithms in detecting and predicting kidney disease. The identification of influential predictors, such as serum creatinine level, blood pressure, and age, underscores their significance in early detection and prognosis. By leveraging machine learning techniques, we can enhance the accuracy and efficiency of kidney disease diagnosis and treatment, ultimately improving patient outcomes and healthcare system effectiveness.

Background/Objectives: Stroke stands as a prominent global health issue, causing con-siderable mortality and debilitation. It arises when cerebral blood flow is compromised, leading to irreversible brain cell damage or death. Leveraging the power of machine learning, this paper presents a systematic approach to predict stroke patient survival based on a comprehensive set of factors. These factors include demographic attributes, medical history, lifestyle elements, and physiological metrics. Method: An effective random sampling method is proposed to handle the highly biased data of stroke. The stroke pre-diction using optimized boosting machine learning algorithms is supported with explainable AI using LIME and SHAP. This enables the models to discern intricate data patterns and establish correlations between selected features and patient survival. Results: The performance of three boosting algorithms is studied for stroke prediction, which include Gradient Boosting (GB), AdaBoost (ADB), and XGBoost (XGB) with XGB achieved the best outcome overall with a training accuracy of 96.97% and testing accuracy of 92.13%. Conclusions: Through this approach, the study seeks to uncover actionable insights to guide healthcare practitioners in devising personalized treatment strategies for stroke patients.

In the design of high speed Flash ADC selection of Thermometer to Binary decoder plays an important role. This paper describes different decoder topologies suitable for Flash ADCs. Comparative analysis between them is presented in terms of hardware required, propagation delay & power consumption. Result shows that fat tree & Mux based topologies are suitable for high speed conversions, but Mux based topology is effective in terms of power consumption. Presence of bubble error reduces output correction capability. In this paper advance scheme is proposed for correction of bubble error in Mux based decoder.

Low resolution and frequency in giga hertz data converters are required for Ultra wide band (UWB) communication. Moreover, complexity of design increases with technology scaling. As communication complexity is increasing, it demands for improved performance and conversion speed while preserving power consumption at a reasonable level. In today's analog world ADC's are having high demand in all applications, mainly in UWB applications. Flash ADC is one of the fastest ADC but it has high power consumption. In this paper, high Speed, low power, and area efficient 3-bit Flash Type ADC using multiplexer based (modified) encoder is proposed in 90nm CMOS technology. And concept of new modified comparator is also introduces. The simulation results show that the proposed circuit consumes low power and require small number of transistor.

With the change in advanced technology and the need for electronic equipments, the development of electronic instruments stimulate more efforts for more innovative ideas and better performance of design. In real world physical values, such as pressure, humidity, temperature and voice can be measured as analog (continuous) signals. However, to process these analog signals by electronic digital equipments, we need to convert these analog signals into digital or discreet signals or binary. In this paper, there are many types of Analog-to-Digital Converters (ADCs) which can be classified according to the applications and concept on which they were designed. ADCs including, Direct conversion ADC (Flash), successive approximation ADC, integrating ADC, Pipeline ADC. In some cases, many comparators are used to reduce the complexity, power and improve the linearity which are also explained in this paper.