Heart failure is considered one of the leading cause death around world. The diagnosis heart a challenging task especially in under-developed and developing countries where there paucity human experts equipments. Hence, different researchers have developed intelligent systems for automated detection failure. However, most these methods are facing problem overfitting i.e. recently proposed improved accuracy on testing data while compromising training data. Consequently, constructed models...

Different automated decision support systems based on artificial neural network (ANN) have been widely proposed for the detection of heart disease in previous studies. However, most these techniques focus preprocessing features only. In this paper, we both, i.e., refinement and elimination problems posed by predictive model, underfitting overfitting. By avoiding model from overfitting underfitting, it can show good performance both datasets, training data testing data. Inappropriate...

Abstract In previous studies, replicated and multiple types of speech data have been used for Parkinson’s disease (PD) detection. However, two main problems in these studies are lower PD detection accuracy inappropriate validation methodologies leading to unreliable results. This study discusses the effects highlights use appropriate alternative methods that would ensure generalization. To enhance accuracy, we propose a two-stage diagnostic system refines extracted set features through...

Parkinson's disease (PD) is the second most common neurodegenerative of central nervous system (CNS). Till now, there no definitive clinical examination that can diagnose a PD patient. However, it has been reported patients face deterioration in handwriting. Hence, different computer vision and machine learning researchers have proposed micrography based methods. But, these methods possess two main problems. The first problem biasedness models caused by imbalanced data i.e. show good...

Cerebrovascular diseases such as stroke are among the most common causes of death and disability worldwide preventable treatable. Early detection strokes their rapid intervention play an important role in reducing burden disease improving clinical outcomes. In recent years, machine learning methods have attracted a lot attention they can be used to detect strokes. The aim this study is identify reliable methods, algorithms, features that help medical professionals make informed decisions...

Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved diagnosis. However, low prediction accuracy still problem in these systems. For better risk accuracy, we propose feature selection method that uses floating window with adaptive size elimination (FWAFE). After the elimination, two kinds classification frameworks are utilized, i.e., artificial neural network (ANN) deep (DNN). Thus, types hybrid proposed this paper,...

During data transmission, a decentralised Mobile Ad Hoc Network (MANET) might result in high Energy Consumption (EC) and short Lifetime (NLife). To address these difficulties, an on-demand Power Load-Aware multipath node-disjoint source routing is presented based on the Enhanced Opportunistic Routing (PLAEOR) protocol. This unique protocol aims at using power, load, latency to manage costs depending control packet flooding from destination node. However, exchange of packets target all nodes...

Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found symptoms might emerge as early ten years before the onset of real disease. As result, machine learning (ML) scientists developed various techniques for prediction using symptoms. However, these methods have fundamental limitations, such low accuracy and bias in models. To resolve issue proposed ML model, we deployed adaptive synthetic sampling (ADASYN)...

Lung cancer is one of the most common causes deaths in modern world. Screening lung nodules essential for early recognition to facilitate treatment that improves rate patient rehabilitation. An increase accuracy during detection vital sustaining persistence, even though several research works have been conducted this domain. Moreover, classical system fails segment cells different sizes accurately and with excellent reliability. This paper proposes a sooty tern optimization algorithm-based...

Dementia is a condition (a collection of related signs and symptoms) that causes continuing deterioration in cognitive function, millions people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining rely primarily on clinical examinations, analyzing medical records, administering neuropsychological testing. However, these methods time-consuming costly terms treatment. Therefore, this study aims present noninvasive method early...

Dementia is a neurological condition that primarily affects older adults and there still no cure or therapy available to it. The symptoms of dementia can appear as early 10 years before the beginning actual diagnosed dementia. Hence, machine learning (ML) researchers have presented several methods for detection based on symptoms. However, these techniques suffer from two major flaws. first issue bias ML models caused by imbalanced classes in dataset. Past research did not address this well...

Nowadays, caesarean section (CS) is given preference over vaginal birth and this trend rapidly rising around the globe, although CS has serious complications such as pregnancy scar, scar dehiscence, morbidly adherent placenta. Thus, should only be performed when it absolutely necessary for mother fetus. To avoid unnecessary CS, researchers have developed different machine-learning- (ML-) based clinical decision support systems (CDSS) prediction using electronic health record of pregnant...

Hepatitis disease is a deadliest disease. The management and diagnosis of hepatitis expensive requires high level human expertise which poses challenges for the health care system in underdeveloped developing countries. Hence, development automated methods accurate prediction inevitable. In this paper, we develop diagnostic hybridizes linear support vector machine (SVM) model with adaptive boosting (AdaBoost) model. We exploit sparsity SVM that caused by <math...

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from progressive cognitive decline that leads to increased morbidity, mortality, disabilities. Since there consensus dementia multifactorial disorder, which portrays changes in the brain of affected individual as early 15 years before onset, prediction models aim at detection risk identification should consider these characteristics. This study aims presenting novel method ten using on data,...

Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers not paid close attention predicting cardiac patient mortality. We developed a clinical decision support system for mortality in patients address this problem. The dataset collected the experimental purposes of model consisted 55 features with total 368 samples. found that classes were highly imbalanced. To avoid problem bias model,...

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become difficult problem medical informatics. The latest studies have attentiveed on feature transformation selection for better prediction. In this study, we assess the performance various extraction algorithms, including principal component analysis (PCA), independent (ICA), locally linear Embedding (LLE), t-distributed stochastic neighbor embedding (TSNE). These algorithms are...