359 research outputs found
Diagnosa Prediksi Penyakit Thypoid Fever Menggunakan Data Mining Dengan Metode Algoritma Naive Bayes Classifier
Wabah penyakit Typhoid Fever di Indonesia memang tengah memuncak, penyakit tersebut disebabkan oleh kuman Salmonella Typosa dan menyebar ke manusia melalui makanan dan minuman yang sudah terkotaminasi. Berdasarkan pada data tahun 2018 awal di RS Budi Asih didapatkan bahwa typhoid fever memasuki 3 besar penyakit yang banyak terjadi selama tahun 2018. Seiring dengan banyaknya pasien kasus typhoid fever akan memungkinkan data dengan jumlah skala yang sangat besar dapat terakumulasi, dengan memanfaatkan data tersebut penulis ingin menerapkan salah satu teknik data mining dengan perhitungan statiska dalam melakukan diagnosis penyakit typhoid fever. Metode yang digunakan adalah Naive Bayes dengan menggunakan sebanyak 250 data pasien kasus typhoid fever. Diagnosa Prediksi Penyakit Typhoid Fever menggunakan metode Naive Bayes merupakan aplikasi juga bertujuan membantu masyarakat dalam mendiagnosis penyakit typhoid fever secara dini. Hasil analisis menunjukkan bahwa gejala demam, mual muntah, pusing, batuk, diare, bradikardi bisa menjadi indikator untuk mendiagnosis penyakit typhoid fever. Hasil analisis juga menunjukkan bahwa ketepatan klasifikasi pasien kasus typhoid fever menggunakan metode naive bayes pada penelitian ini adalah sebesar 92%
Directional naive Bayes classifiers
Directional data are ubiquitous in science.
These data have some special properties that rule out the
use of classical statistics. Therefore, different distributions
and statistics, such as the univariate von Mises and the
multivariate von Mises–Fisher distributions, should be
used to deal with this kind of information. We extend the
naive Bayes classifier to the case where the conditional
probability distributions of the predictive variables follow
either of these distributions. We consider the simple scenario,
where only directional predictive variables are used,
and the hybrid case, where discrete, Gaussian and directional
distributions are mixed. The classifier decision
functions and their decision surfaces are studied at length.
Artificial examples are used to illustrate the behavior of the
classifiers. The proposed classifiers are then evaluated over
eight datasets, showing competitive performances against
other naive Bayes classifiers that use Gaussian distributions
or discretization to manage directional data
Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed
in Euclidean space. There are, however, numerous contexts of considerable
scientific interest in which the natural supports for the data under
consideration are Riemannian manifolds like the unit circle, torus, sphere and
their extensions. Typically, such data can be represented using one or more
directions, and directional statistics is the branch of statistics that deals
with their analysis. In this paper we provide a review of the many recent
developments in the field since the publication of Mardia and Jupp (1999),
still the most comprehensive text on directional statistics. Many of those
developments have been stimulated by interesting applications in fields as
diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics,
image analysis, text mining, environmetrics, and machine learning. We begin by
considering developments for the exploratory analysis of directional data
before progressing to distributional models, general approaches to inference,
hypothesis testing, regression, nonparametric curve estimation, methods for
dimension reduction, classification and clustering, and the modelling of time
series, spatial and spatio-temporal data. An overview of currently available
software for analysing directional data is also provided, and potential future
developments discussed.Comment: 61 page
Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics
Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE
A Cheat Sheet for Bayesian Prediction
This paper reviews the growing field of Bayesian prediction. Bayes point and
interval prediction are defined and exemplified and situated in statistical
prediction more generally. Then, four general approaches to Bayes prediction
are defined
and we turn to predictor selection. This can be done predictively or
non-predictively and predictors can be based on single models or multiple
models. We call these latter cases unitary predictors and model average
predictors, respectively. Then we turn to the most recent aspect of prediction
to emerge, namely
prediction in the context of large observational data sets and discuss three
further classes of techniques. We conclude with a summary and statement of
several current open problems.Comment: 33 page
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