Analysis of Machine Learning Models for Heart Disease Prediction using Different Algorithms: A Review

Now a days the heart diseases are growing very rapidly making it an important and apprehensive task of prediction of these kinds of diseases in advance. The diagnosis is also a tough chore because it has to be performed in a precise and efficient manner. The emerging technology in modern life style integrated with internet of thing which having sensors and huge amount of data is sent to various clouds for further investigation using different algorithms to fetch out precise information for various domains. Across the world approximately 3 quintillion bytes/day information generated and this data stored for further examination. As data is in huge quantity therefore, appropriate methods applied to examine the perfect analysis so that prediction can be carried out optimally. Clinical decision making is dominant to all patient care happenings which includes choosing a deed, between replacements. These days emerging field like Machine Learning play prime role in healthcare to analyze and predict the diseases. After investigating numerous research article on Machine Learning, it was found that for same data set accuracy was different for various algorithms. In our research work different machine learning techniques will be implemented and will be tested for various parameters like accuracy, precision, recall on validated dataset. ML and Neural Networks are more capable in supporting deciding and predicting from the enormous data formed by health care systems

Toward enhancement of deep learning techniques using fuzzy logic: a survey

Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed

A New Evolutionary Algorithm For Mining Noisy, Epistatic, Geospatial Survey Data Associated With Chagas Disease

The scientific community is just beginning to understand some of the profound affects that feature interactions and heterogeneity have on natural systems. Despite the belief that these nonlinear and heterogeneous interactions exist across numerous real-world systems (e.g., from the development of personalized drug therapies to market predictions of consumer behaviors), the tools for analysis have not kept pace. This research was motivated by the desire to mine data from large socioeconomic surveys aimed at identifying the drivers of household infestation by a Triatomine insect that transmits the life-threatening Chagas disease. To decrease the risk of transmission, our colleagues at the laboratory of applied entomology and parasitology have implemented mitigation strategies (known as Ecohealth interventions); however, limited resources necessitate the search for better risk models. Mining these complex Chagas survey data for potential predictive features is challenging due to imbalanced class outcomes, missing data, heterogeneity, and the non-independence of some features. We develop an evolutionary algorithm (EA) to identify feature interactions in Big Datasets with desired categorical outcomes (e.g., disease or infestation). The method is non-parametric and uses the hypergeometric PMF as a fitness function to tackle challenges associated with using p-values in Big Data (e.g., p-values decrease inversely with the size of the dataset). To demonstrate the EA effectiveness, we first test the algorithm on three benchmark datasets. These include two classic Boolean classifier problems: (1) the majority-on problem and (2) the multiplexer problem, as well as (3) a simulated single nucleotide polymorphism (SNP) disease dataset. Next, we apply the EA to real-world Chagas Disease survey data and successfully archived numerous high-order feature interactions associated with infestation that would not have been discovered using traditional statistics. These feature interactions are also explored using network analysis. The spatial autocorrelation of the genetic data (SNPs of Triatoma dimidiata) was captured using geostatistics. Specifically, a modified semivariogram analysis was performed to characterize the SNP data and help elucidate the movement of the vector within two villages. For both villages, the SNP information showed strong spatial autocorrelation albeit with different geostatistical characteristics (sills, ranges, and nuggets). These metrics were leveraged to create risk maps that suggest the more forested village had a sylvatic source of infestation, while the other village had a domestic/peridomestic source. This initial exploration into using Big Data to analyze disease risk shows that novel and modified existing statistical tools can improve the assessment of risk on a fine-scale

The effectiveness of birth plans in increasing use of skilled care at delivery and postnatal care in rural Tanzania: a cluster randomised trial.

OBJECTIVES: To determine the effectiveness of birth plans in increasing use of skilled care at delivery and in the postnatal period among antenatal care (ANC) attendees in a rural district with low occupancy of health units for delivery but high antenatal care uptake in northern Tanzania. METHODS: Cluster randomised trial in Ngorongoro district, Arusha region, involving 16 health units (8 per arm). Nine hundred and five pregnant women at 24 weeks of gestation and above (404 in the intervention arm) were recruited and followed up to at least 1 month postpartum. RESULTS: Skilled delivery care uptake was 16.8% higher in the intervention units than in the control [95% CI 2.6-31.0; P = 0.02]. Postnatal care utilisation in the first month of delivery was higher (difference in proportions: 30.0% [95% CI 1.3-47.7; P < 0.01]) and also initiated earlier (mean duration 6.6 ± 1.7 days vs. 20.9 ± 4.4 days, P < 0.01) in the intervention than in the control arm. Women's and providers' reports of care satisfaction (received or provided) did not differ greatly between the two arms of the study (difference in proportion: 12.1% [95% CI -6.3-30.5] P = 0.17 and 6.9% [95% CI -3.2-17.1] P = 0.15, respectively). CONCLUSION: Implementation of birth plans during ANC can increase the uptake of skilled delivery and post delivery care in the study district without negatively affecting women's and providers' satisfaction with available ANC services. Birth plans should be considered along with the range of other recommended interventions as a strategy to improve the uptake of maternal health services

Anthropometry and mortality : a cohort study of rural Bangladeshi women.

Many authors suggest that low anthropometric levels are associated with higher mortality risk in adults, In developing countries however there have been few opportunities to test this hypothesis. In addition, there is increasing interest in the role of women's nutritional status in their own health and survival as distinct from its impact on infant outcomes. This thesis describes the results obtained from a longitudinal historical follow-up of a cohort of 2,314 rural Bangladeshi women over a period of 19 years (1975-1993). The demographic, socio-economic, and anthropometric characteristics of the study cohort are described with reference to the methods of data extraction, preparation and validation. The risk of mortality associated with different levels of the anthropometric indicators (height, weight, arm circumference and body mass index) were analysed using Cox's proportional hazards models. In addition to the basic survival models, the effects of confounding, early mortality, missing data, and young subjects, on the estimates are discussed. A significant association between BMI and mortality (p=0.009) was found in adjusted analyses which used categories that distinguished the women in the highest and lowest 10% of the cohort BMI distribution. Women with BMI levels between 10% and 90% and >90% had hazard ratios of 0.45 (95% confidence intervals 0.27,0.73) and 0.55 (0.25,1.22) respectively, when compared to women with BMI <10%. The strength of the association between BMI and mortality risk was reduced after adjusting the models for early mortality (<4 years), (p=0.068). No significant associations were found between height, arm circumference and mortality risk. In conclusion, these data provide no evidence that these anthropometric indicators would be useful in population-based screening programmes in rural Bangladesh to identify women at higher mortality risk. The findings are considered with respect to the study's methodological constraints and comparisons with other studies in order to produce recommendations for those working in research and health programmes in women's nutrition

Molecular characterization of neurofibromatosis type 1–associated malignant peripheral nerve sheath tumours and functional identification of genes involved in their pathogenesis

[cat] El tumor maligne de la beina dels nervis perifèrics (MPNST) és un sarcoma de teixit tou del sistema nerviós perifèric. Al voltant del 50% dels MPNSTs es desenvolupen en el context de la neurofibromatosi de tipus 1 (NF1) a partir de neurofibromes plexiformes (pNFs) preexistents. Mentre que els pNFs no mostren grans alteracions genòmiques, els MPNSTs presenten un genoma hiperploide amb diverses alteracions somàtiques en el nombre de còpies (SCNAs), que són recurrents. En aquesta tesi s'hipotetiza que les SCNAs tenen un impacte en l'expressió gènica de manera regional, i que aquesta informació transcriptòmica pot ser emprada per trobar gens implicats en la patogènesi dels MPNSTs. L'objectiu principal d'aquesta tesi ha estat la caracterització del genoma dels MPNSTs i la utilització de la informació genòmica regional per a la identificació de gens i mecanismes moleculars implicats en la patogènesi dels MPNSTs associats a la NF1. En la caracterització genòmica es va desenvolupar un assaig de qPCR per a la detecció de delecions constitucionals i somàtiques del gen NF1. Un segon assaig de qPCR va ser dissenyat per a la detecció de SCNAs en MPNSTs. Els SNP arrays d'un grup de MPNSTs van confirmar el genoma hiperploide i les SCNAs recurrents d'aquest tipus de tumors. A partir de dades de microarrays d'expressió d'un altre grup de MPNSTs es van identificar regions del genoma amb una abundància significativa de gens sobre- o infraexpressats, coneguts com a desequilibris transcripcionals (Tls). Es va trobar una associació global dels Tls amb les SCNAs, sobretot dels Tls de sobreexpressió amb guanys genòmics. Experiments de RNAi i assajos de genètica funcional en línies cel-lulars derivades de MPNSTs semblaven indicar que els Tls, tot i incloure algun gen "driver" de la tumorigènesi dels MPNSTs, capturarien principalment expressió de tipus "passenger". La utilització d'informació exclusiva proporcionada pels Tls va facilitar la identificació de gens candidats i possibles mecanismes moleculars implicats en la patogènesi dels MPNSTs, com ara la borealina i el "cromosomal passenger complex", i algunes kinesines mitòtiques, com K1F11, K1F15 i KIF23. A més, es va trobar la kinesina K1F11 com a nova potencial diana terapèutica per al tractament dels MPNSTs.[eng] Malignant peripheral nerve sheath tumour (MPNST) is a soft tissue sarcoma of the peripheral nervous system. Around 50% of MPNSTs develop in the context of the hereditary cancer neurofibromatosis type 1 (NF1), normally arising from pre-existing benign plexiform neurofibromas (pNFs). While pNFs do not show gross genomic alterations, MPNSTs present a hyperploid genome with recurrent somatic copy number alterations (SCNAs). We hypothesized that these SCNAs have an impact on gene expression in a regional manner, which can be informative when studying MPNST pathogenesis. This regional transcriptomic information could be employed for finding genes involved in the pathogenesis of MPNSTs, including some potential therapeutic targets. The main objective of this thesis project was to characterize the MPNST genome and use regional genomic information by the integration of DNA copy number and gene expression in order to identify genes and molecular mechanisms involved in the pathogenesis of MPNSTs arising in the context of NF1. Copy number analysis was performed by using SNP array and two DNA-based qPCR assays. A probe-based qPCR assay was developed for an accurate detection of constitutional and somatic deletions in the NF1 gene. A second qPCR assay was designed to detect SCNAs in MPNSTs, where genomic repetitive sequences were found to improve the normalization of copy number data. The analysis of the genome of a set of MPNSTs by SNP array confirmed a hyperploid genome with recurrent SCNAs for these tumours. ln addition, expression microarray data from a different set of tumours was used to identify regions of the MPNST genome with a significant abundance of over- or underexpressed genes, known as transcriptional imbalances (Tls). These Tls were found to be globally associated with the identified recurrent SCNAs, especially for Tls of overexpression with genomic gains. Tls of underexpression were found to be associated with genomic losses and significatively enriched in genes that were hypermethylated in MPNSTs compared to benign pNFs. All together these results indicated a remarkable impact of regional genomic copy number alterations (and probably also a regional epigenomic status) on the expression of the genes contained in these regions. RNAi experiments and functional genetic approaches used in MPNST cell lines seemed to indicate that Tls, despite probably including one or few drivers of MPNST pathogenesis, would be mainly capturing passenger gene expression. T1 information, together with previous biological knowledge of cancer genes in other tumour types, was used to search drivers of MPNST pathogenesis encompassed within Tls. Furthermore, the exclusive information provided by Tls was used to select genes with an opposed differential expression to the T1 overall expression, which facilitated the identification of candidate genes and molecular mechanisms for MPNST pathogenesis: borealin and the chromosomal passenger complex seemed to be involved in MPNST pathogenesis, and some mitotic kinesins, such as K1F11, K1F15 and KIF23, were also proposed as important players. ln addition, K1F11 was found to be a novel potential therapeutic target for the treatment of MPNSTs

Algorithms for regression and classification

Regression and classification are statistical techniques that may be used to extract rules and patterns out of data sets. Analyzing the involved algorithms comprises interdisciplinary research that offers interesting problems for statisticians and computer scientists alike. The focus of this thesis is on robust regression and classification in genetic association studies. In the context of robust regression, new exact algorithms and results for robust online scale estimation with the estimators Qn and Sn and for robust linear regression in the plane with the estimator least quartile difference (LQD) are presented. Additionally, an evolutionary computation algorithm for robust regression with different estimators in higher dimensions is devised. These estimators include the widely used least median of squares (LMS) and least trimmed squares (LTS). For classification in genetic association studies, this thesis describes a Genetic Programming algorithm that outpeforms the standard approaches on the considered data sets. It is able to identify interesting genetic factors not found before in a data set on sporadic breast cancer and to handle larger data sets than the compared methods. In addition, it is extendible to further application fields

A survey on explainable anomaly detection

NWOAlgorithms and the Foundations of Software technolog

Maintaining Momentum to 2015? An impact evaluation of interventions to improve maternal and child health and nutrition in Bangladesh

Bangladesh has experienced rapid fertility decline and reductions in under-five mortality over the last three decades. This impact study unravels the various factors behind these changes. Economic growth has been important, but so have major public sector interventions, notably reproductive health and immunization, supported by external assistance from the World Bank and other agencies. By contrast, nutrition began to improve only in the 1990s and remains high. The Bangladesh Integrated Nutrition Program (BINP) has played a small role, if any, in this progress, which is mainly attributable to higher agricultural productivity.Bangladesh, mortality, fertility, nutrition, health, population