A stroke occurs when the blood supply to a person's brain is interrupted or reduced. The deprives of oxygen and nutrients, which can cause cells die. Numerous works have been carried out for predicting various diseases by comparing performance predictive data mining technologies. In this work, we compare different methods with our approach prediction on Cardiovascular Health Study (CHS) dataset. Here, decision tree algorithm used feature selection process, principle component analysis...

Abstract Purpose of the study : Cerebrovascular accident (CVA) occurs quickly and disrupts brain blood flow, causing neurological problems. Risk factors include smoking, diabetes, hyperlipidemia, hypertension, atrial fibrillation. Many stroke prediction systems are computationally demanding, sluggish, unreliable. This uses interpolation spline curves that impute data Principal Component Analysis to identify most important for prediction. Using stack ensemble machine learning methods, goal is...

Abstract One of the most fatal and prevalent diseases central nervous system is a brain tumour. Different subgrades exist for each type tumour because broad variety tumours pathologies. Manual diagnosis may be error‐prone time‐consuming, both which are becoming more challenging as medical community's workload grows. There need automatic diagnosis. In this study, we have proposed deep learning model (MultiFeNet) based on convolutional neural network classification tumours. MultiFeNet uses...

Congestive heart failure (CHF) is one of the primary sources mortality and morbidity among global population. Over 26 million individuals globally are affected by disease, its prevalence rising 2% yearly. With advances in healthcare technologies, if we predict CHF early stages, leading factors can be reduced. Therefore, main objective this study to use machine learning applications enhance diagnosis reduce cost employing minimum features forecast possibility a occurring. We employ deep...

Abstract Cancer is a major threat to human health; thus, early detection imperative for successful management. Rapid diagnosis can be achieved by imaging primary (subcutaneous) tumors using fluorophores conjugated with tumor markers. Here, the application of biocompatible, quantum efficient, monodisperse, and photostable polymer-coated dots (PQDs) demonstrated targeted prostate in living SCID mice. Briefly, PQDs (blue) are folic acid (FA-PQDs) DCC-NHS chemistry. Initially, vitro via FA-PQDs...

Assistant Conversational Agents (chatbots) are becoming increasingly complex and sophisticated since they enabled to understand respond human language. Chatbots have varying organizational functions, from customer support personal assistance, dominated the digital market as efficient effective. can collect valuable information customers through interaction which result in a better experience, but an inappropriate or inadequate response also lead loss of customers. Therefore, chatbots must be...

COVID-19 is an infectious disease that has cost millions of lives all over the world. A faster and safer diagnosis highly desirable in order to stop its spread. An electrocardiogram (ECG) signal-based shown potential cardiac, stroke, COVID-19. In this study, ensemble three deep learning models are used for detection ECG images multi-class classification. The results obtained with weighted average technique have been improved by using grid search technique. For classification, optimized...

Stroke is one of the main causes death and disability in elderly people worldwide. Early diagnosis this disease desirable. An electrocardiogram (ECG) signal a powerful tool stroke. Many recent studies are using ECG signals This study done with aim to develop an efficient classification model which can be used stroke features. uses data collected from 71 different subjects 35 were patients 36 normal patients. proposes stacked ensemble built by stacking three convolutional neural networks...

This research endeavours to optimize cardiac anomaly detection by introducing a method focused on selecting the most effective Daubechis wavelet families. The principal aim is differentiate between states that are normal and abnormal utilizing longer electrocardiogram (ECG) signal events based Apnea ECG dataset. often used detect sleep apnea, disorder characterized repeated interruptions in breathing during sleep. By using machine learning methods, such as Principal Component Analysis (PCA)...

This article introduces a large handwritten text document image corpus dataset for Urdu script named CALAM (Cursive And Language Adaptive Methodologies). The database contains unconstrained sentences along with their structural annotations the offline images XML representation. is fourth most frequently used language in world, but due to its complex cursive writing and low resources, it still thrust area analysis. Here, unified approach applied development of an by collecting printed texts,...