Machine Learning Approach for Predicting Stages of Chronic Kidney Disease

Advantages And Disadvantages Of Chronic Kidney Disease
MACHINE LEARNING APPROACH FOR PREDICTING STAGES OF CHRONIC KIDNEY
DISEASE ABSTRACT Chronic kidney disease refers to the kidneys have been damaged by
conditions, such as diabetes, glomerulonephritis or high blood pressure. It also creates more possible
to mature heart and blood vessel disease. These problems may happen gently, over a long period of
time, often without any symptoms. It may ultimately lead to kidney failure requiring dialysis or a
kidney transplant to preserve survival time. So the early detection and treatment can prevent or
deferral of these complications. One of the main tasks is giving proper treatment and accurate
diagnosis of the disease. The major problem is finding an accurate algorithm which doesn't require
long time to run for ... Show more content on Helpwriting.net ...
RELATED WORK Miguel A et al [8]. Proposed a distributed approach for the management of
alarms related to monitoring CKD patients within the eNefro project. The results proof the
pragmatism of Data Distribution Services (DDS) for the activation of emergency protocols in terms
of alarm ranking and personalization, as well as some observations about security and privacy.
Christopher et al [9]. Deliver a contextualized method and possibly more interpretable means of
communicating risk information on complex patient populations to time–constrained clinicians.
Dataset was collected from American Diabetes Association (ADA) of 22 demographic and clinical
variables related to heart attack risk for 588 people with type2 diabetes. The method and tool could
be encompasses to other risk–assessment scenarios in healthcare distribution, such as measuring
risks to patient safety and clinical recommendation compliance. Srinivasa R. Raghavan et al [10].
Briefly reviews the literature on clinical decision support system, debates some of the difficulties
faced by practitioners in managing chronic kidney failure patients, and sets out the decision
provision techniques used in developing a dialysis decision support
... Get more on HelpWriting.net ...

A Survey On Data Mining Classification Algorithms
A Survey on Data Mining Classification Algorithms
Abstract: Classification is one of the most familiar data mining technique and model finding process
that is used for transmission the data into different classes according to particular condition. Further
the classification is used to forecast group relationship for precise data instance. It is generally
construct models that are used to predict potential statistics trends. The major objective of machine
data is to perfectly predict the class for each record. This article focuses on a survey on different
classification techniques that are mostly used in data–mining.
Keywords: Data mining, Classification, decision tree, neural network.
1. INTRODUCTION
Data mining is one of the many ... Show more content on Helpwriting.net ...
Classification contains finding rules that partition the data into disjoint groups patterns and process.
The goal of classification is to evaluate the input data to develop a precise. Explanation or model for
each class using the features by using the present data.
2. ARCHITECTURE OF DATA MINING
Data mining and knowledge discovery is the name frequently used to refer to a very
interdisciplinary field, which consists of using methods of several research areas to extract
knowledge from real–world datasets. There is a distinction between the terms data mining and
knowledge discovery which seems to have been introduced by [Fayyad et al.1996].the term data
mining refers to the core step of a broader process, called knowledge discovery in database.
Architecture of data mining structure is defined the following figure.
3. DATA MINING PROCESS
 Data cleaning
 Data integration
 Data selection
 Data transformation
 Pattern evaluation
 Knowledge presentation.
Data cleaning: Data cleaning or data scrubbing is the process of detecting as well as correcting (or
removing) inaccurate data from a record set. It handles noisy data it represents random error in
attribute values. In very large dataset noise can come in many shapes and forms. And irrelevant data
handles the missing and unnecessary data in the source file.
Data integration: Data integration process contains the data from multiple sources.

Text Analytics And Natural Language Processing
IV. SENTIMENT ANALYSIS A. The Sentiment analysis process i) Collection of data ii)
Preparation of the text iii) Detecting the sentiments iv) Classifying the sentiment v) Output i)
Collection of data: the first step in sentiment analysis involves collection of data from user. These
data are disorganized, expressed in different ways by using different vocabularies, slangs, context of
writing etc. Manual analysis is almost impossible. Therefore, text analytics and natural language
processing are used to extract and classify[11]. ii) Preparation of the text : This step involves
cleaning of the extracted data before analyzing it. Here non–textual and irrelevant content for the
analysis are identified and discarded iii) Detecting the sentiments: All the extracted sentences of the
views and opinions are studied. From this sentences with subjective expressions which involves
opinions, beliefs and view are retained back whereas sentences with objective communication i.e
facts, factual information are discarded iv) Classifying the sentiment: Here, subjective sentences are
classified as positive, negative, or good, bad or like, dislike[1] v) Output: The main objective of
sentiment analysis is to convert unstructured text into meaningful data. When the analysis is
finished, the text results are displayed on graphs in the form of pie chart, bar chart and line graphs.
Also time can be analyzed and can be graphically displayed constructing a sentiment time line with
the chosen

Measuring A Computational Prediction Method For Fast And...
In general, the gap is broadening rapidly between the number of known protein sequences and the
number of known protein structural classes. To overcome this crisis, it is essential to develop a
computational prediction method for fast and precisely determining the protein structural class.
Based on the predicted secondary structure information, the protein structural classes are predicted.
To evaluate the performance of the proposed algorithm with the existing algorithms, four datasets,
namely 25PDB, 1189, D640 and FC699 are used. In this work, an Improved Support Vector
Machine (ISVM) is proposed to predict the protein structural classes. The comparison of results
indicates that Improved Support Vector Machine (ISVM) predicts more accurate protein structural
class than the existing algorithms.
Keywords–Protein structural class, Support Vector Machine (SVM), Naïve Bayes, Improved
Support Vector Machine (ISVM), 25PDB, 1189, D640 and FC699.
I. INTRODUCTION (HEADING 1)
Usually, the proteins are classified into one of the four structural classes such as, all–α, all–β, α+β,
α/β. So far, several algorithms and efforts have been made to deal with this problem. There are two
steps involved in predicting protein structural classes. They are, i) Protein feature representation and
ii) Design of algorithm for classification. In earlier studies, the protein sequence features can be
represented in different ways such as, Functional Domain Composition (Chou And Cai, 2004),
Amino Acids

Using Different Types of Stemmer
Experimental Setup
Preprocessing Data
Preprocessing is actually a trail to improve text classification by removing worthiness information.
In our work document preprocessing involve removing punctuation marks, numbers, words written
in another language, normalize the documents by (replace the letter ("‫آ‬ ‫إ‬ ‫أ‬ ") with (‫)""ا‬, replace the
letter (‫ؤ‬ ‫"ء‬ ") with (""‫)ا‬, and replace the letter("‫)"ى‬ with (""‫)ا‬. Finally removing the stop words,
which are words that can be found in any text like prepositions and pronouns. The rest of words are
returned and are referred to as keywords or features. The number of these features is usually large
for large documents and therefore some filtering can be applied to these features to reduce their
number and remove redundant features.
Features Extraction
Text is categorized by two types of features, external and internal. External features are not related
to the content of the text, such as author name, publication date, author gender, and so on. Internal
features reflect the text content and are mostly linguistic features, such as lexical items and
grammatical categories[www]. In our work, words were treated as a feature on three levels: using a
bag of words form, word stem, in which the suffix and prefix were removed and word root. With all
these features we need to extract and generates the frequency list of the dataset features (single
words) and save it in a training file.
Feature Selection
The output of feature extraction step is

Comparative Study Of Classification Algorithms
Comparative Study of Classification Algorithms used in Sentiment Analysis
Amit Gupte, Sourabh Joshi, Pratik Gadgul, Akshay Kadam
Department of Computer Engineering, P.E.S Modern College of Engineering
Shivajinagar, Pune amit.gupte@live.com Abstract–The field of information extraction and retrieval
has grown exponentially in the last decade. Sentiment analysis is a task in which you identify the
polarity of given text using text processing and classification. There are various approaches in the
task of classification of text into various classes. Use of particular algorithms depends on the kind of
input provided. Analyzing and understanding when to use which algorithm is an important aspect
and can help in improving accuracy of results.
Keywords– Sentiment Analysis, Classification Algorithms, Naïve Bayes, Max Entropy, Boosted
Trees, Random Forest.
I. INTRODUCTION
In this paper we have presented a comparative study of most commonly used algorithms for
sentimental analysis. The task of classification is a very vital task in any system that performs
sentiment analysis. We present a study of algorithms viz. 1. Naïve Bayes 2.Max Entropy 3.Boosted
Trees and 4. Random Forest Algorithms. We showcase the basic theory behind the algorithms, when
they are generally used and their pros and cons. The reason behind selecting only the above
mentioned algorithms is the extensive use in various tasks of sentiment analysis. Sentiment analysis
of reviews is very common application, the

Dynamic News Classification Using Machine Learning
Dynamic News Classification using Machine Learning Introduction Why this classification is
needed ? (Ashutosh) The exponential growth of the data may lead us to a time in future where huge
amount of data would not be able to be managed easily. Text Classification is done through Text
Mining study which would help sorting the important texts from the content or a document to
manage the data or information easily. //Give a scenario, where classification would be mandatory.
Advantages of classification of news articles (Ayush) Data classification is all about tagging the data
so that it can be found quickly and efficiently.The amount of disorder data is increasing at an
exponential rate, so if we can build a machine model which can automatically classify data then we
can save time and huge amount of human resources. What you have done in this paper (all) Related
work In this paper [1] , the author has classified online news article using Term Frequency–Inverse
Document Frequency (TF–IDF) algorithm.12,000 articles were gathered & 53 persons were to
manually group the articles on its topics. Computer took 151 hours to implement the whole
procedure completely and it was done using Java Programming Language.The accuracy of this
classifier was 98.3 % . The disadvantages of using this classifier was it took a lot of time due to
large number of words in the dictionary. Sometimes the text contained a lot of words that described
another category since the

Essay On Content Mining
Introduction:
Content mining is tied in with recovering important data from information or content accessible in
accumulations of reports through the recognizable proof and investigation of fascinating examples.
This procedure is particularly beneficial when a client needs to locate a specific kind of incredible
data on the web. Content mining is focusing on the record accumulation. The greater part of content
mining calculation and methods are gone for finding designs crosswise over extensive record
accumulations. The quantity of records can extend from the large number to millions. This paper
goes for talking about some essential content mining strategies, calculation, and apparatuses.
Background:
There are right now excessively various ... Show more content on Helpwriting.net ...
The objective of K–implies calculation is to separate M focuses in N measurements into K bunches.
It looks for a K–parcel with locally ideal inside bunch total of squares by moving focuses called
Euclidean separation starting with one bunch then onto the next [3]. It is a well–known bunch
examination method for investigating a dataset.
2– Naïve Bayes Classifier Algorithm
Characterization in information mining is an assignment of anticipating an estimation of all out
factors. It should be possible by building models in view of a few factors or highlights for the
expectation of a class of a protest on the premise of its traits [4]. A standout amongst the most well–
known learning strategy gathered by likenesses is Naïve Bayes Classifier. It is an administered
machine learning calculations that work on the Bayes Theorem of Probability to manufacture
machine learning models. Guileless Bayes Classifier is extremely useful for breaking down printed
information, for example, Natural Language Processing. It chips away at restrictive likelihood.
What's more, it is the likelihood that something will happen, given that something else has just
happened. By using this, the client will have the capacity to compute the likelihood of a something
utilizing its information [5].
3– Apriori Algorithm
The Apriori algorithm is an important algorithm for mining repeated elements collections especially
for Boolean association rules. It practices methodology known as "bottom up",

Data Extraction Of Knowledge From High Volume Of Data Essay
Introduction:
Data mining is extraction of knowledge from high volume of data. In this data stream mining
experiment, I have used "sorted.arff" dataset contains 540888 instances and 22 attributes. I have
tried two single algorithms and two ensemble algorithms, tested the accidents on road for last 15
years.
Weka: Data Mining Software
Weka ("Waikato Environment for knowledge Analysis") is a collection of algorithms and tools used
for data analysis. The algorithms can be applied directly or it can be called using java code, an
object oriented programming language. It contains tools for pre–processing, classification,
regression, clustering, associating, select attributes and visualization on given dataset. The
advantages of using WEKA software is, it is freely available and platform independent. It is simple
tool and it can be used by non–specialist of data mining. For testing, it doesn't need any
programming code at all.
WEKA can identify .arff file format. It can classify the dataset present in .arff file. First open the file
sorted.arff, second, test the file with few algorithms with respect to accuracy and finally predict the
value of D1 factor. The screenshot 1 is the pre–processing of 22 attributes in Weka and last attribute
D1 factor is analysed using algorithms.
Screenshot 1: Graphs of pre–processed data
Algorithms Considered:
There are different types of machine learning logarithms available to solve the classification
problems. To carry out this experiment

Generic Strategy For Classifying A Text Document
.1 Generic Strategy for Classifying a Text Document The main steps involved are i) document pre–
processing, ii) feature extraction / selection iii) model selection iv) Training and testing the
classifier. Information pre–preparing lessens the measure of the information content records
essentially. It includes exercises like sentence limit determination [2], characteristic dialect
particular stop–word disposal [1] [2] [3] and stemming [2] [4]. Stop–words are practical words
which happen as often as possible in the dialect of the content (for instance, „a‟, ‟the‟, ‟an‟, ‟of‟
and so forth in English dialect), so they are not helpful for order. Stemming is the activity of
decreasing words to their root or base structure. For English dialect, the Porter‟s stemmer is a
prominent calculation [4] [12], which is an addition stripping arrangement of orderly strides for
stemming an English word, decreasing the vocabulary of the preparation content by around 33% of
its unique size [4]. For instance, utilizing the Porter‟s stemmer, the English word "speculations"
would in this manner be stemmed as "speculations → speculation → sum up → general → gener".
In situations where the source records are site pages, extra pre–handling is required to evacuate/alter
HTML and other script labels [13]. Highlight extraction/choice distinguishes essential words in a
content report. This is done utilizing strategies like TF–IDF (term recurrence opposite report
recurrence) [14], LSI (dormant

The Static Model Of Data Mining Essay
Abstract: Lot of research done in mining software repository. In this paper we discussed about the
static model of data mining to extract defect .Different algorithms are used to find defects like naïve
bayes algorithm, Neural Network, Decision tree. But Naïve Bayes algorithm has the best result
.Data mining approach are used to predict defects in software .We used NASA dataset namely, Data
rive. Software metrics are also used to find defects.
Keywords: Naïve Bayes algorithm, Software Metric, Solution Architecture,.
I. INTRODUCTION
According to [1], multiple algorithms are combined to show better prediction capability using votes.
Naïve Bayes algorithm gives the best result if used individual than others. The contribution of this
paper based on two reasons. Firstly, it provides a solution architecture which is based on software
repository and secondly it provides benchmarks that provide an ensemble of data mining models in
the defective module prediction problem and compare the result. Author used NASA dataset online
[2] which contain five large software projects with thousands of modules. Bohem found 80/20 rule
and about the half modules are defect free [3]. Fixing Defects in the operational phase is
considerably more expensive than doing so in the development or testing phase. Cost–escalation
factors ranges from 5:1 to 100:1 [3]. It tells defects can be fixed in operational phase not in
development and testing phase. The study of defect prediction can be classified into

The Average Age Of Mothers
Scenario
The designed database is used to record the births of all premature babies in a Hospital. Created
database consists of four tables which are also referred as the dimensions of the database, and these
tables are Patient, Duration, Doctor and Baby. Each dimension will consist of at least 5 attributes.
With the use of this database Doctors are able to keep track of individual babies' records and analyse
different patterns such as how long a premature baby does needs medical attention and in average
how many babies are admitted to this ward annually.
________________________________________
Objectives
To list the average time scale baby would be in the hospital
To list the average gender of babies recovers quickly
To list the ... Show more content on Helpwriting.net ...
SQL Queries
Patient Table
The SQL code is written to present all patients surnames within the database, and the above
screenshot shows the SQL code and the results from the query.
Baby Table
The SQL code is written to provide all babies names along with their surname, and the above
screenshot shows the SQL code and the results from the query.
Doctor Table
The SQL code is written to provide all Doctors names within the database along with their surname,
I've customised this code so the names are sorted in alphabetical order and the above screenshot
shows the SQL code and the results from the query.
Duration Table
This is simple SQL query which is used to present records from a specific table. The above
screenshot shows the SQL code and the results from Duration table.
Average number of survival
As part of my objectives I needed find an average of number of babies whom survived from the

records of the database, order to that I used the average function on SQL to run a query showing
how many babies have survived from the 10 records.
Number of babies survived
I also stated in my objective that I would use COUNT function on my database therefore I used the
table containing babies' records to write SQL function that will run and count how many babies have
survived from the 10 records.
Multi Dimension Array
Array 1
Array 2
Task 2
Mining association rules

Malicious Software Or Malware?
Introduction
Malwares
Malicious software or malware is software designed for malicious purposes.Some malware may
delete, overwrite, or steal user data. In general, this type of software can cause damage to the user's
computer and may steal vital information.Since this is a broad definition, malware can be classified
into categories such as viruses, worms, trojan horses, spyware, adware, or botnets. Since there is
substantial overlap between these type of malware, we refer to them simply as "viruses". We can
further classify viruses based on the way they try to conceal themselves from being detected by
antivirus programs . These categories are "encrypted," "polymorphic," and "metamorphic."
2.1 Encrypted Viruses
"Encrypted viruses" refer to those viruses that encrypt their body using a specified encryption
algorithm but using different keys at every infection. Each encrypted virus has a decryption routine
that usually remains the same, despite the fact that the keys change between infections. Therefore, it
is possible to detect this class of viruses by analyzing the decryptor in order to obtain a reasonable
signature. Figure 1 shows an encrypted virus example. Encrypted viruses tend to use simple
algorithms for encryption. Common variants use algorithms such as XORing the body of the virus
with the encryption key. Despite its effort to encrypt its body, this type of viruses can be easily
detected by signature detection.
Fig 2 illustrates a simple encryption code written

The And Temporal Information Extraction
Much of the information extraction community early focus on in tasks like named entity
recognition, co–reference and relation extraction, but recently due to the high demand of temporal
information in different NLP application, scholars are focusing on event, temporal and event and
temporal information extraction and the extraction of event and temporal information became a hot
research area. Different scholars involved in event and temporal information extraction researches.
Currently there are a lot of research conducted in event and temporal information extraction in
different domains and languages with different techniques, methods and tools. In this chapter, we
present some of the works conducted in the English language related to this thesis project. The first
work we discuss is called TIE, Temporal Information Extraction system extracts events from text by
inducing as much as temporal information possible. TIE makes global inference, enforcing
transitivity to bound the beginning and finishing time for each event. TIE introduces temporal
entropy as a method to evaluate the performance of temporal IE system. TIE system outperforms in
experiment in three optional approaches. The TIE system uses a probabilistic method to recognize
temporal rations. They use TimeBank data [25] to train the system. TIE processes, each natural
language sentence into two sequential phases. The first phase is responsible for extracting event and
identifying temporal expression and it use a

Concrete Gravity Dams
Table of Contents Table of Contents 1 1. Introduction 2 2. Literature Review 2 3. Classification
Techniques in Machine Learning 3 3.1 K–nearest Neighbor 3 3.2 Support Vector Machine 4 3.3
Naïve Bayes Classifier 5 References 8 Introduction Dams are important structures to supply water
for irrigation or drinking, to control flood, and to generate electricity. The safety analysis of concrete
gravity dams in seismic regions is significant due to the high potential of life and economic losses if
these buildings fail. Many of existing dams were built using outdated analysis methods and lack of
knowledge (Bernier 2016). In the design of many existing dams, dam–water–foundation interactions
affecting the earthquake response were not ... Show more content on Helpwriting.net ...
Another study is carried out by Gaspar et al. in order to investigate the effects of uncertainties of
dam properties. A thermos–mechanical model was developed to define the behavior. Classification
Techniques in Machine Learning The classification of the data is the problem to observe new
numbers of discrete categories. The aim is to learn a model making accurate predictions on new
observations based on a set of data points. For example, for the observations set, x1, ..., xn, the
corresponding categories, y1, ..., yn, where yi ∈{–1, +1}. Here, yi=–1 refers to category of failure
region, and yi=+1 refers to safe region. A new observation, x value, is assigned to one of these
categories. The three popular classification techniques are explained: (1) K–nearest neighbor
(KNN), (2) support vector machine (SVM), and (3) naïve Bayes classifier (NBC). The given
algorithms here include both deterministic and probabilistic classification approaches. 3.1 K–nearest
Neighbor In the machine learning, one of the simplest method for classification is the K–nearest
neighbor algorithm. Given a new observation x∈R, K training observations from the rows of the
Xtm closest in distance to x are found. After that, using the mainstream division among these K
nearest observations from the training set, x is classified. Consequently, the performance of KNN
algorithm depends on the choice of K and the algorithm is sensitive to local structure of

Nt1330 Unit 1 Algorithm
Brief: We were assigned to construct a software that utilizes a classification algorithm that is able to
accurately decide a correct classification for a certain sequence of inputs that were provided by the
user. The input is to be classified based on a known training set of records of the same attributes as
the sequence provided by the user. Method used: We were instructed to use naive Bayes classifier
for data classification but no certain variation was provided. Minding that the dataset that was
provided contained continuous data, there was two obvious approaches available handle to the
situation: 1– Use a variation of naive Bayes that handles continuous data which features Gaussian
(normal) distribution or reconstructing the ... Show more content on Helpwriting.net ...
I extracted the testing set by taking the last 15 records of each class for testing. Division
Programming languages: For implementation I used Java and used derby database to manipulate the
dataset. Using derby database instead of using the file directly may seem irrelevant and irrational but
it allowed me to use SQL code which is ideal with manipulating bulks of data. It also grants
flexibility and maintainability to the dataset and allows it to be swiftly modified updated or altered.
Using SQL and avoiding over complicating my code helped me to easily trace the code as
mathematical equations was a big part of the assignment and each mean, variance (standard
deviation) and normal distribution function had to be recalculated by hand to ensure that no logical
or mathematical errors existed. Thus making my overall approach to the assignment much more

Breast Cancer : A Leading Cause Of Death And Major Health...
Introduction
Cancer is a leading cause of death and major health problem all over the world. According to the
statistics from World Health Organization (WHO), more than 10 million people are diagnosed with
cancer and about 6 million will die from these disease each year. Among all the cancers, breast
cancer is the most widespread malignancy in women. In 2013, Breast cancer was estimated to claim
close to 40,000 lives with over 200,000 new cases in American women, making it major cancer
mortality reason among women (Ping, Siegal, Almeida, Schnitt & Shen, 2014). There is still no
known cause for breast cancer however some of the known risk factors are family history, alcohol
abuse, aging, and obesity. Preventing breast cancer is still an issue but early diagnoses can help stop
the cancer from spreading to other parts of the body and also reduce breast cancer deaths.
Breast tumors can be either "benign" (non–cancerous) or "malignant" (cancerous) and the type of
the tumor can be identified by using the Tumor, Nodes and Metastasis (TNM) staging system and
the disease stage can range from stage I to stage IV. In the stage 0 (carcinoma in situ), there is a high
probability of developing invasive form of cancer in a short period of time. This staging system of
breast cancer allows treatment options without surgical biopsy and therefore it is one of the most
important factors in detecting breast tumors. Thus, a reliable procedure is required to accurately
distinguish and diagnose

Advantages Of Naive Bayes Classifier
IV. NAIVE BAYES CLASSIFIERS
Naïve Bayes classifier is a simple but effective the Bayesian classifier built upon the strong
assumption that different features are independent with each other. Classification is done by
selecting the highest posterior of classification variable given a set of feature. Naive Bayes
classifiers assume that the effect of a variable value on a given class is independent of the values of
other variable. This assumption is called class conditional independence. An advantage of the naive
Bayes classifier is that it requires a small amount of training data to estimate the variable values
necessary for classification. 1. Each data sample is represented by an n dimensional feature vector,
X = (x1, x2..... xn), depicting n ... Show more content on Helpwriting.net ...
A set of cases was taken and the program was trained with these data sets such that the probabilities
of all the classes with all the conditions were calculated. Result was stored in database and when the
test data was given we got the probabilities for the various classes for the given symptom values on
the basis of which we inferred that the patient fell into the class with the highest probability. This is
what is called the Naïve Bayes‟ classification. This is a very powerful technique that is instrumental
in helping us predict the category a patient falls into.
Swine flu is presumptively diagnosed clinically by the patient's history of association with people
known to have the disease and their symptoms listed above. Usually, a quick test (for example,
nasopharyngeal swab sample) is done to see if the patient is infected with influenza A or B virus.
Most of the tests can distinguish between A and B types. The test can be negative (no flu infection)
or positive for type A and B. If the test is positive for type B, the flu is not likely to be swine flu. If it
is positive for type A, the person could have a conventional flu strain or swine flu.

Data Mining Information About Data
Abstract– Data Mining extracts useful information about data. In other words, Data Mining extracts
the knowledge or interesting information from large set of structured data that are from different
sources. Data mining applications are used in a range of areas such as it is used for financial data
analysis, retail and telecommunication industries, banking, health care and medicine. In health care,
the data mining is mainly used for disease prediction. In data mining, there are several techniques
have been developed and used for predicting the diseases that includes data preprocessing,
classification, clustering, association rules and sequential patterns. This paper analyses the
performance of two classification techniques such as Bayesian ... Show more content on
Helpwriting.net ...
The medical data processing has the high potential in medical domain for extracting the hidden
patterns within the dataset [15]. These patterns are used for clinical diagnosis and prognosis. The
medical data are generally distributed, heterogeneous and voluminous in nature. An important
problem in medical analysis is to achieve the correct diagnosis of certain important information.
This paper describes classification algorithms and it is used to analyze the performance of these
algorithms. The accuracy measures are True Positive (TP) rate, F Measure, Receiver Operating
Characteristics (ROC) area and Kappa Statistics. The error measures are Mean Absolute Error
(M.A.E), Root Mean Squared Error (R.M.S.E), Relative Absolute Error (R.A.E) and Relative Root
Squared Error (R.R.S.E) [5]. Section 2 explains the literature review; Section 3 describes the
classification algorithms. Experimental results are analyzed in section 4 and section 5 illustrates the
conclusion of this paper.
II. LITERATURE REVIEW Dr. S.Vijayarani et al., [11] determined the performance of various
classification techniques in data mining for predicting the heart disease from the heart disease
dataset. The classification algorithms is used and tested in this work. The performance factors
evaluate the efficiency of algorithms, clustering accuracy and error rate. The result illustrates
LOGISTICS classification function efficiency is better than multilayer perception and sequential

Classification Between The Objects Is Easy Task For Humans
Classification between the objects is easy task for humans but it has proved to be a complex
problem for machines. The raise of high–capacity computers, the availability of high quality and
low–priced video cameras, and the increasing need for automatic video analysis has generated an
interest in object classification algorithms. A simple classification system consists of a camera fixed
high above the interested zone, where images are captured and consequently processed.
Classification includes image sensors, image preprocessing, object detection, object segmentation,
feature extraction and object classification. Classification system consists of database that contains
predefined patterns that compares with detected object to classify in to proper category. Image
classification is an important and challenging task in various application domains, including
biomedical imaging, biometry, video surveillance, vehicle navigation, industrial visual inspection,
robot navigation, and remote sensing. Fig. 1.1 Steps for image classification Classification process
consists of following steps a) Pre–processing– atmospheric correction, noise removal, image
transformation, main component analysis etc. b) Detection and extraction of a object– Detection
includes detection of position and other characteristics of moving object image obtained from
camera. And in extraction, from the detected object estimating the trajectory of the object in the
image plane. c) Training: Selection of the

Summary Of Software Prefects
Abstract Early predictions of software defects during software development life cycle play a vital
role to produce successful software project. Besides, it reduces the cost of software development,
maintains high quality, improves software testing and facilitates procedures to identify defect–prone
of software packages. We need more research studies today to improve convergence across research
studies due to its use a different of inconsistent measurements. Therefore, to avoid several types of
bias: different sizes of the dataset are used in our study to avoid the bias of data. Comparative
studies relied on appropriate accuracy indicators suitable for the software defect model are utilized
to overcome the problems of bias error in ... Show more content on Helpwriting.net ...
Therefore, if we can identify which modules in software most probably to be defective it can help us
to reduce our limited resources and time of development [2]. Based on the specific types of software
metrics in the previous research studies, numbers of predictive classification model are proposed for
software quality it can explore the defectives in some of the software modules by using several types
of classifiers such as decision tree [3], SVM [4], ANN [5] and Naïve Bayes [6]. The classification
model is one of the most common approaches used for early prediction of software defects [7],
including two categories: Fault–Prone (FP) software and Non–Fault–Prone (NFP) software. The
prediction model represented by a set of code attribute or software metrics [7, 8]. The objective of
this research is utilized ensemble learning method that combines the vote of multiple learner
classifiers by using the different subset of feature to improve the accuracy of supervised learning.
Another advantage of ensemble methods, it enhances the performance by using different types of
classifiers together because it reduces the variance between them and keeps bias error rate without
increasing. Two main types of ensemble methods [9]: Bagging and Boosting techniques [10]. The
first one depends on subsampling of the training dataset by replacement sample and generates
training subset then

Natural Language Processing ( Nlp )
CHAPTER1: INTRODUCTION
Natural Language Processing (NLP) deals with actual text element processing. The text element is
transformed into machine format by NLP. Artificial Intelligence (AI) uses information provided by
the NLP and applies a lot of maths to determine whether something is positive or negative. Several
methods exist to determine an author's view on a topic from natural language textual information.
Some form of machine learning approach is employed and which has varying degree of
effectiveness. One of the types of natural language processing is opinion mining which deals with
tracking the mood of the people regarding a particular product or topic. This software provides
automatic extraction of opinions, emotions and sentiments in text and also tracks attitudes and
feelings on the web. People express their views by writing blog posts, comments, reviews and
tweets about all sorts of different topics. Tracking products and brands and then determining
whether they are viewed positively or negatively can be done using web. The opinion mining has
slightly different tasks and many names, e.g. sentiment analysis, opinion extraction, sentiment
mining, subjectivity analysis, affect analysis, emotion analysis, review mining, etc.
However, they all come under the umbrella of sentiment analysis or opinion mining. Sentiment
classification, feature based sentiment classification and opinion summarization are few main fields
of research predominate in sentiment analysis.
In

A Brief Note On Random Forest Tree Based Approach It Uses...
COMPARISON OF CLASSIFIERS
CLASSIFIER CATEGORY DESCRIPTION REFERENCE
Naive Bayes Probability based classifier This classifier is derived from Naïve Bayes conditional
probability. This is suitable for datasets having less number of attributes. [5]
Bayesian Net Probability based classifier Network of nodes based on Naïve Bayes classifier is
termed as Bayesian Net. This can be applied to larger datasets as compared to Naïve Bayes. [9]
Decision Tree (J48) Tree based approach It is enhanced version of C 4.5 algorithm and used ID3.
[15]
Random Forest Tree based approach It is also a decision tree based approach but have more
accuracy as compared to J48. [15]
Random Tree Tree based approach It generates a tree by randomly selecting branches from ... Show
more content on Helpwriting.net ...
a similar development leads rules extraction techniques to make poorer sets of rules. DT algorithms
perform a variety method of nominal attributes and can 't handle continuous ones directly. As result,
an outsized variety of ml and applied math techniques will solely be applied to information sets
composed entirely of nominal variables. However, an awfully giant proportion of real information
sets embrace continuous variables: that 's variables measured at the interval or magnitude relation
level. One answer to the present drawback is to partition numeric variables into variety of sub–
ranges and treat every such sub–range as a class. This method of partitioning continuous variables
into classes is sometimes termed discretization. Sadly, the quantity of how to discretize a continual
attribute is infinite. Discretization could be a potential long bottleneck, since the variety of attainable
discretization is exponential within the number of interval threshold candidates at intervals the
domain [14]. The goal of discretization is to seek out a collection of cut points to partition the range
into a little variety of intervals that have sensible category coherence, that is sometimes measured by
an analysis operate. Additionally to the maximization of reciprocality between category labels and
attribute values, a perfect discretization technique

Evaluation Of Classification Methods For The Prediction Of...
Evaluation of Classification Methods for the Prediction of Hospital Length of
Stay using Medicare Claims data.
Length of stay in hospital determines the quality of care and safety of patients. It depends on various
factors and varies among medical cases with different conditions and complications. Length of stay
depends on factors such as age, sex, co–morbidities, time between surgery and mobilization,
severity of
illness, etc [1]. It can be assumed that reduced length of stay hospital is associated with better health
results and good quality of care. Length of stay in hospital also depends upon the quick response to
the
emergency medical case. The earlier the response the less is the length of stay and less likelihood of
death ... Show more content on Helpwriting.net ...
Unsupervised learning in data mining helps in clustering the data
by determining their similarity, helping patterns to emerge. The supervised learning is used to
classify
new unknown data [4–7].
For the above scenarios, SPSS software [8] was for statistical analysis and WEKA 3.7 for
classification
software [9].
The data that was used for analysis is a 2012 Medicare Provider Analysis and Review (MEDPAR)
file.
The first scenario assumes that the patient has just been admitted and the information known about
the

patient is as follows:
1>Patient demographics
2>Information about their hospital
3>Admission information
The following assumptions are made for the second scenario:
1>The patient has already been hospitalized for a few days.
2>Diagnosis is already known.
3>Procedures and other related hospital information.
Let's consider the first LOS cut–off point is 4 days and the second LOS cut off point is 12 days.
Here we
predict the LOS, if it is equal to 4days then it is approximately about 50% percentile of the LOS
distribution and LOS equal to 12 days is approximately 90% percentile of the LOS distribution. In
this
experiment the performance of three classifiers are compared. The three classifiers used are Naïve
Bayes,
AdaBoost and C4.5 decision tree. The first classifier used is Naïve Bayes it is a probabilistic
classifier
based on Bayes theorem. The second classifier used is the Adaboost

The Importance Of Classifiers
After removing the useless attributes, we are left with 640 attributes. When we retrain the Naive
Bayes and J48 classifiers using this reduced dataset, we got the same accuracy as we got in A(d) that
is before reducing the attributes. The accuracy of Classifiers on the reduced dataset: ⦁ Naive Bayes:
Accuracy = 70.85% ⦁ J48: Accuracy = 72.95% As we can see the accuracy of both the classifiers is
intact even after removing many attributes. Here we can observe that performance of both the
classifiers remains same, it neither improves nor degrade, this proves that the removed attribute
didn't contribute to the performance of both the classifiers and didn't help classifiers in classifying
the instances. Thus, such dimensions should be ... Show more content on Helpwriting.net ...
⦁ As we can see the difference in plots of the attributes against the class variable for the highest
ranked attributes(for example, pixel_14_15) and the lowest ranked ones(pixel_2_11), in the former
plot we can see that if we select this attribute and split it using some value range then we will get
more meaningful sub–datasets and we can use those sub–datasets to classify the instances however
in the second case if select that attribute and try to split it, we will get impure data and we won't be
able to classify data as almost all instances have similar value for pixel_2_11 attribute. Here, the
first attribute has high information gain however the second attribute has 0 information gain. ⦁ It's
not safe to remove all the attributes with Information gain as 0. We should remove the attributes
with IG 0 and run the classifier to make sure that performance has not degraded by removing those
attributes. For example, In classifier's result we can see the 'Incorrectly Classified Instances', we
started removing the attributes with IG 0 and retrained the classifier with new dataset and checked
the value of incorrectly classified instances to make sure that that value does not increase and if it
does and even after removing more attributes it does not come back to global low value then we
should retrieve those attributes back and remove only those attributes when we had lowest
Incorrectly Classified

The Diagnosis Of Heart Disease
DATA MINING IN THE DIAGNOSIS OF HEART DISEASE
Data mining as it has been established is simply a way of getting hidden knowledge or information
from data, the technique(s) employed search for concurrency, relationships, and outliers in this data
that they present as knowledge [4]. This knowledge can then be used in different applications.
Prediction is one of the ways that the hidden knowledge gotten from the data can be used. The main
aim is to use a large number of past values to consider probable future [9]. Heart disease is the
common term for a variety of diseases, conditions, and disorders that affect the heart and the blood
vessels [6]. Symptoms of heart disease vary depending on the type of heart disease. Congenital heart
disease suggests to a problem with the heart 's structure and function due to abnormal heart
development before birth. Congestive heart failure is when the heart does not pump adequate blood
to the other organs in the body [8]. Coronary heart disease or in its medical term Ischemic heart
disease is the most frequent type of heart problem [24]. Coronary heart disease is a term that refers
to damage to the heart that happens because its blood supply is decreased; it leads to fatty deposits
build up on the linings of the blood vessels that provide the heart muscles with blood, resulting in
heart failure. Heart disease prediction using data mining is one of the most interesting and
challenging tasks. Shortage of specialists and high wrongly diagnosed

Essay On Sentiment Classification
The aspect–level sentiment analysis overcomes this problem and performs the sentiment
classification taking the particular aspect into consideration. There can be a situation where the
sentiment holder may express contrasting sentiments for the same product, object, organization etc
Techniques for sentiment analysis is generally partitioned as (1) machine learning approach, (2)
lexicon–based approach and (3) combined approach (Meddhat et al., 2014a). There are two
approaches for the lexicon–based approach. First one is the dictionarybased approach and the
second one is the corpus–based approach that utilizes the factual or semantic strategy for
discovering the polarity. The dictionary–based approach is based on finding the sentiment seed ...
Show more content on Helpwriting.net ...
Some combined rule algorithms were proposed in (Medhat et al., 2008a). Therefore, a study on
decision tree and decision rule problem is done by Quinlan (1986). Probabilistic Classifier
Probabilistic classifiers make the utilization of blend of models for classification. Every class is
considered to be a component of the mixed model. We have described various probabilistic
classifiers for sentiment analysis problem in the next subsection. 4.1.1.4.1 Naive Bayes Classifier
(NB). It is the frequently used classifier in sentiment analysis. In sentiment analysis, naive Bayes
classifier calculates the posterior probability of either positive class or negative class depending on
the sentiment words distributed over the document. The work of naïve Bayes classifier is based on
the Bag–of–word extraction of features in which the word's position is overlooked in the whole text.
This classifier uses the Bayes theorem. It calculates the probability for the sentiment word in a
document and tells whether that word belongs to the positive or negative class. The probability can
be calculated using the given formula. This assumption results in Bayesian Network. A Bayesian
network is a directed acyclic graph containing nodes and edges, where nodes denote the random
variables and the edges denote the conditional dependencies. It is a conditional exponential classifier
that takes the feature sets with label and converts them into

Classification Of Data Mining Techniques
Abstract
Data mining is the process of extracting hidden information from the large data set. Data mining
techniques makes easier to predict hidden patterns from the data. The most popular data mining
techniques are classification, clustering, regression, association rules, time series analysis and
summarization. Classification is a data mining task, examines the features of a newly presented
object and assigning it to one of a predefined set of classes. In this research work data mining
classification techniques are applied to disaster data set which helps to categorize the disaster data
based on the type of disaster occurred in worldwide for past 10 decade. The experimental
comparison has been conducted among Bayes classification algorithms (BayesNet and NaiveBayes)
and Rules Classification algorithms (DecisionTable and JRip). The efficiency of these algorithms is
measured by using the performance factors; classification accuracy, error rate and execution time.
This work is carried out in the WEKA data mining tool. From the experimental result, it is observed
that Rules classification algorithm, JRip has produced good classification accuracy compared to
Bayes classification algorithms. By comparing the execution time the NaiveBayes classification
algorithm required minimum time.
Keywords: Disasters, Classification, BayesNet, NaiveBayes, DecisionTable, JRip.
I Introduction
Data mining is the process of extracting hidden information from the large dataset. Data mining is

Breast Cancer: A Brief Summary
Breast cancer is one of the leading types of cancer in females and is the fifth most common cause of
cancer death. In the year of 2013, American Cancer Society (ACS) reported that 39,620 women and
410 men, a total of 40,030 people, passed away due to breast cancer.
Breast cancer is the most common invasive type of cancer among women. Many machine learning
and pattern recognition techniques have been proposed to detect the breast cancer. One of these
techniques is Bayes classifier. In this paper naïve bayes classifier is used to detect the breast cancer.
Naïve Bayesian (NB) is also known as a simple classifier, which is based on the Bayes theorem. In
this paper, a new NB (weighted NB) classifier was used and its application on breast cancer

The Importance Of Textual Technology
Mihalceaet. al.‟s work was able to break 70% classification accuracy by using a bag–of–words
model, and 10–fold cross–validation with Naive Bayes and SVM (support vector machine).Cardieet.
al. Using a combination of LIWC, bigram and unigram features, they were capable to achieve
89.8% classification accuracy using 5– fold cross validation with linear SVM on written textual data
[5] . Another study by (Ben Verhoeven &WalterDaelemans, in 2014) designed to serve multiple
purposes: disclosure of age, gender, authorship, personality, feelings, deception, subject and gender.
Another major feature is the planned annual expansion with new students each year. The corpus
currently has about 305,000 codes distributed on 749 documents. The average ... Show more content
on Helpwriting.net ...
The spam detection has different domains including Web (Castillo et al., 2006), Email (Chirita,
Diederich, &Nejdl, 2005), and SMS (Karami& Zhou, 2014a, 2014b). The problem of opinion spam
was raised by investigating supervised learning techniques to detect fake reviews (Jindal & Liu,
2008). Lim et al. (2010) tracked the Spam behavior, and found certain behaviors such as targeting
specific products or product groups to maximize impact (Lim, Nguyen, Jindal, Liu, &Lauw, 2010).
Using machine learning techniques is one of the popular approaches in online review spam
detection. Some examples are employing standard word and part–of–speech (POS) n–gram features
for supervised learning (Ott et al., 2011), using a graph–based method to find fake store reviewers
(Wang, Xie, Liu, & Yu, 2011), and using frequent pattern mining to find groups of reviewers who
often write reviews together (Mukherjee, Liu, Wang, Glance, & Jindal, 2011; Mukherjee, Liu, &
Glance, 2012) [8] . The Most Effective Technique in the literature comes by (AmirKaram& Bin
Zhou in 2015) which incorporated an additional 224 features, LIWC+, based on based on the
different sets of the raw features collected from LIWC. For example, derive relative polarity by
examining the difference between the degrees of positive and negative emotions. Call this extension
of LIWC

Implementation Of The Machine Learning Classifier For...
chapter{Implementation}
Implementation of the machine learning classifier for anomaly detection involves using some
libraries which help to execute the different steps to classify data and perform analysis. In the next
section the detailed implementation of this project will be discussed.
section{Dataset selection}
This was the most important part of the entire project and consumed a lot of time. Selecting a
suitable database to perform the desired type of analysis was a very difficult task as there are a very
few well organized and labeled medical databases which are suitable to perform anomaly detection.
The database used by this project is the Pima Indians Diabetes database cite{Dataset} which is a
well structured and a labeled ... Show more content on Helpwriting.net ...
This helps to display the data points, attributes and various features in the dataset.
The other library that is used in the project is Scikit–learn cite{scikit}. This library plays an
important role in building the classifier and designing the machine learning algorithm. Scikit–learn
cite{scikit} is an open source library which provides efficient tools for data mining and data
analysis. It is based on other Python libraries like NumPy cite{Numpy}, Scipy cite{Scipy} and
Matplotlib cite{Matplot}. Scikit cite{scikit} provides various functions and methods for
classification and hence helps to build an efficient classifier to detect anomalies in the data.
section{Machine learning algorithms}
There is a vast collection of machine learning classifiers that are provided in the Scikit–learn library
cite{scikit}. All that is needed to do is install and import the Scikit–learn cite{scikit} library. Three
different machine learning algorithms are used to build a classification model to detect anomalies in
the data. Out of these three the one which provides optimal accuracy is chosen. The machine
learning algorithms that are used on the data are the Gaussian Naive Bayes algorithm, Logistic
Regression algorithm and Support Vector Machine algorithm.
After loading the dataset, the next important step is to visualize the data. Further, there is a need to
split the data into training and

Phishing Is A Social Engineering Luring Technique
Phishing is a social engineering luring technique, in which an attacker aims to steal sensitive
information such as credit card information and online banking passwords from users. Phishing is
carried over electronic communications such as email or instant messaging. In this technique, a
replica of the legitimate sites is created, and the users are directed to the Phishing pages where it's
required for the personal information.
As phishing remains a significant criminal activity that causes great loss of money and personal
data. In respond to these threats, a variety of Anti–Phishing tools was released by software vendors
and companies. The Phishing detection technique used by industries mainly includes attack tracing,
report generating filtering, analysing, authentication, report making and network law enforcement.
The toolbars like spoof guard, trust watch, spoof stick and Net–craft are some of the most popular
Anti–Phishing toolbars services that are used. The W3C has set some standards that are followed by
most of the legit websites, but the phishing site may not develop these standards. There are certain
characteristics of URL and source code of the Phishing site based on which we can guess the fake
site.
The goal of this paper is to compare different methods that are used to determine the phishing web
pages to safeguard the web users from attackers. In [2] lexical signature is extracted from a given
web page based on several unique terms that is used as a dataset

A Machine Learning Approach For Emotions Classification
A machine learning approach for emotions classification in Micro blogs ABSTRACT Micro
blogging today has become a very popular communication tool among Internet users. Millions of
users share opinions on different aspects of life every day. Therefore micro blogging web–sites are
rich sources of data for opinion mining and sentiment analysis. Because micro blogging has
appeared relatively recently, there are a few research works that are devoted to this topic.In this
paper, we are focusing on using Twitter, which is an amazing microblogging tool and an
extraordinary communication medium for text and social web analyses.We will try to classify the
emotions in to 6 basic discrete emotional categories such as anger, disgust, fear, joy, sadness and
surprise. Keywords : Emotion Analysis; Sentiment Analysis; Opinion Mining; Text Classification 1.
INTRODUCTION Sentiment analysis or opinion mining is the computational study of opinions,
sentiments and emotions expressed in text. Sentiment analysis refers to the general method to
extract subjectivity and polarity from text.It uses a machine learning approach or a lexicon based
approach to analyse human sentiments about a topic..The challenge for sentimental analysis lies in
identifying human emotions expressed in these text. The classification of sentiment analysis goes as
follows: Machine Learning is the field of study that gives computer the ability to learn without being
explicitly programmed. Machine learning explores the

Essay On Machine Learning Classifiers And Feature Extractors
3. Methods and Experimental design
Our approach is to analyse the sentiments using machine learning classifiers and feature extractors.
The machine learning classifiers are Naive Bayes, Maximum Entropy and Support Vector Machines
(SVM). The feature extractors are unigrams and unigrams with weighted positive and negative
keywords. We build a framework that treats classifiers and feature extractors as two distinct
components. This framework allows us to easily try out different combinations of classifiers and
feature extractors.
3.1 Emoticons
Since the training process makes use of emoticons as noisy labels, it is crucial to discuss the role
they play in classification. we striped the emoticons out from the training data. If we leave ... Show
more content on Helpwriting.net ...
Repeated letters Tweets contain very casual language. For example, if you search "hungry" with an
arbitrary number of u‟s in the middle (e.g. huuuungry, huuuuuuungry, huuuuuuuuuungry) on
Twitter, there will most likely be a nonempty result set. We use preprocessing so that any letter
occurring more than two times in a row is replaced with two occurrences. In the samples above,
these words would be converted into the token "hungry".
Table 2 shows the effect of these feature reductions. These reductions shrink the feature set down to
8.74% of its original size. Table 2. Effect of Feature Reduction
Feature Reduction Steps # of Features Percentage of Original
None 277354 100.00%
URL / Username / Repeated Letters 102354 36.90%
Stop Words („a‟, „is‟, „the‟) 24309 8.74%
Final 24309 8.74%
3.3 Feature Vector
After preprocessing the training set data which consists of 9666 positive tweets, 9666 negative
tweets and 2271 neutral tweets, we compute the feature vector as below:
Unigrams As shown in Table 2, at the end of preprocessing we end up with 24309 features which are
unigrams and each of the features have equal weights.
4. Results
The unigram feature vector is the simplest way to retrieve features from a tweet. The machine

learning algorithms perform average with this feature vector. One of the reasons for the average
performance might be the smaller training

Essay On ACO
ACO simulates the behavior of real ants. The first ACO technique is known as Ant System and it
was applied to the travelling salesman problem. Since then, many variants of this technique have
been produced. ACO is a probabilistic technique that can be applied to generate solutions for
combinatorial optimizations problems. The artificial ants in the algorithm represent the stochastic
solution construction procedures which make use of the dynamic evolution of the pheromone trails
that reflects the ants' acquired search experience and the heuristic information related to the problem
in hand, in order to construct probabilistic solutions [15].
In order to apply ACO to test case generation, a number of issues need to be addressed, namely,
I. ... Show more content on Helpwriting.net ...
Problems identified based on reviewed research papers are:
As the dimensionality of the Attribute space increases, many types of data analysis and classification
also become significantly harder, and, additionally, the data becomes increasingly sparse in the
space it occupies which can lead to big difficulties for both supervised and unsupervised learning.
A large number of Attributes can increase the noise of the data and thus the error of a learning
algorithm, especially if there are only few observations (i. e., data samples) compared to the number
of Attributes.
In the last years, several studies have focused on improving feature selection and dimensionality
reduction techniques and substantial progress has been obtained in selecting, extracting and
constructing useful feature sets. However, due to the strong inuence of different feature subset
selection methods on the classification accuracy, there are still several open questions in this
research field. Moreover, due to the often increased number of candidate features for various
application areas new questions arise.
Curse of dimensionality:
Following various problems occurs when searching in or estimating density on high–dimensional
spaces–
1. Estimation: In a multidimensional grid, the problem of calculating a density function on a high–
dimensional space may be seen as finding the density at each

The Sentiment Analysis Review
Abstract– Sentiment analysis is the computational study of opinions, sentiments, subjectivity,
evaluations, attitudes, views and emotions expressed in text. Sentiment analysis is mainly used to
classify the reviews as positive or negative or neutral with respect to a query term. This is useful for
consumers who want to analyse the sentiment of products before purchase, or viewers who want to
know the public sentiment about a new released movie. Here I present the results of machine
learning algorithms for classifying the sentiment of movie reviews which uses a chi–squared feature
selection mechanism for training. I show that machine learning algorithms such as Naive Bayes and
Maximum Entropy can achieve competitive accuracy when trained using features and the publicly
available dataset. It analyse accuracy, precision and recall of machine learning classification
mechanisms with chi–squared feature selection technique and plot the relationship between number
of ... Show more content on Helpwriting.net ...
Feature Selection
The next step in the sentiment analysis is to extract and select text features. Here feature selection
technique treat the documents as group of words (Bag of Words (BOWs)) which ignores the position
of the word in the document.Here feature selection method used is Chi–square (x2).
A chi–square test also referred to as a statistical hypothesis test in which the sampling distribution of
the test statistic is a chi–square distribution when the null hypothesis is true. The chi–square test is
used to determine whether there is a significant difference between the expected frequencies and the
observed frequencies in one or more categories.
Assume n be the total number of documents in the collection, pi(w) be the conditional probability of
class i for documents which contain w, Pi be the global fraction of documents containing the class i,
and F(w) be the global fraction of documents which contain the word w. Then, the x2–statistic of the
word between word w and class i is defined[1]

Application Of C5. 0 Algorithm To Flu Prediction Using...
Application of C5.0 Algorithm to Flu Prediction Using Twitter Data
LZ M. Albances, Beatrice Anne S. Bungar, Jannah Patrize O. Patio, Rio Jan Marty S. Sevilla,
*Donata D. Acula
University of Santo Tomas, Philippines, {lz.albances.iics, beatriceanne.bungar.iics,
jannahpatrize.patio.iics, riojanmarty.sevilla.iics, ddacula}@ust.edu.ph
Abstract – Since one's health is a factor considered, data coming from Twitter, one of the most
popular social media platforms often used by millions of people, is beneficial for predictions of
certain diseases. The researchers created a system that will improve the precision rate of the current
system conducted by Santos and Matos using C5.0 algorithm instead of Naive Bayes algorithm for
classifying tweets ... Show more content on Helpwriting.net ...
Twitter provides free APIs from which a sampled view can be easily obtained. It helps build a map
spread model. In line with this, the researchers came up with an idea of working on a research using
these Twitter data, focusing on the so–called "tweets" regarding diseases and health, specifically, the
flu or influenza.
Furthermore, previous studies were made regarding this topic. In 2008, there was a website called
"Google Flu Trends" that took data from Google, Facebook, and Twitter itself [2]. It was widely
used in its release, but it was proven to have inconsistencies with regards to its data [2].
Back in 2011–2013, Google Flu Trends overestimated the number of people who had flu by 50%
[3]. One possible cause was its inclusion of data which contained the word "flu" or "influenza",
wherein the person who "tweeted" or posted about it did not have the flu at all [3].
There was also a study that made a precision of 0.78 or 78% using the Naïve Bayes Classification
algorithm to identify tweets that mention flu or flu–like symptoms [4]. As mentioned earlier,
previous researchers had difficulty classifying these tweets as some data gathered got accepted even
though the person did not have it.
According to the Department of Health of the Philippines [5], getting flu is possible by contacting
items that are contaminated through the discharges of an infected

Pt1420 Unit 3 Data Mining Assignment
Amr Wael
Data mining Assignment
125873
What is Naïve Bayes?
Naïve Bayes is a classifier based on Bayes theorem, this classifier assumes that the result of an
attribute value on a class is independent of the other attributes values.
In Bayes' theorem, we want to compute the probability to P (Class|Predictor) which can be
expressed in terms of probabilities:
P (Class|Predictor) = P (Predictor|Class) P (Class) / P (Predictor)
P (Class|Predictor) : the posterior probability
P (Predictor|Class) : the likelihood
P (Class) : the prior probability of class
P (Predictor) : the prior probability of predictor
(Patterson, 2011)
As we are working on Iris data set which have continuous values, there is a must to change the
continuous ... Show more content on Helpwriting.net ...
Calculate the probability of every user input of the four attributes belonging to a class
Multiply all the probabilities related to every class and get the Maximum one to be the class that
maximize the input
References
Chen, E. (2011, April 27). Choosing a Machine Learning Classifier. Retrieved from blog.echen.me:
http://blog.echen.me/2011/04/27/choosing–a–machine–learning–classifier/
Hockenmaier, J. (2012). Lecture 3 : Smoothing. Retrieved from courses.engr.illinois.edu:
https://courses.engr.illinois.edu/cs498jh/Slides/Lecture03HO.pdf
Hun Yun, Kyeong–Mo Hwang, Chan–Kyoo Lee . (2013, July 26). Development of Safety Factors
for the UT Data Analysis Method in Plant Piping. Retrieved from file.scirp.org/pdf:
http://file.scirp.org/pdf/WJNST_2013100817024698.pdf
Patterson, J. (2011, May 2). Classification with Naive Bayes. Retrieved from www.slideshare.net:

Case Study On Popular Data Mining
Study on Popular Data Mining Algorithms/Techniques
Abstract: Data mining algorithms determine how the cases for a data mining model are analyzed.
Data mining model algorithms provide the decision–making capabilities needed to classify,
segment, associate and analyze data for the processing of data mining columns that provide
predictive, variance, or probability information about the case set. With an enormous amount of data
stored in databases and data warehouses, it is increasingly important to develop powerful tools for
analysis of such data and mining interesting knowledge from it. Data mining is a process of
inferring knowledge from such huge data. Data Mining has three major components Clustering or
Classification, Association Rules and Sequence Analysis.
Keywords: Data mining, Classification, Clustering, Algorithms
Introduction:
Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks.
In general, data mining tasks can be classified into two ... Show more content on Helpwriting.net ...
The selection and implementation of the appropriate data – mining technique is the main task in this
phase. This process is not straightforward; usually, in practice, the implementation is based on
several models, and selecting the best one is an additional task. By simple definition, in
classification/clustering we analyze a set of data and generate a set of grouping rules which can be
used to classify future data. One of the important problems in data mining is the Classification–rule
learning which involves finding rules that partition given data into predefined classes. An
association rule is a rule which implies certain association relationships among a set of objects in a
database. In sequential Analysis, we seek to discover patterns that occur in sequence. This deals with
data that appear in separate

Machine Learning Approach for Predicting Stages of Chronic Kidney Disease

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