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Implementation of K-Nearest Neighbors Algorithm for Predicting Heart Disease Using Python Flask

     Heart disease is a non-communicable disease and the number 1 cause of death in Indonesia. According to WHO predictions, heart disease will cause 11 million deaths in 2020. Bad lifestyle and unhealthy consumption patterns of modern society are the causes of this disease experienced by many people. Lack of knowledge about heart conditions and the potential dangers cause heart disease attacks before any preventive measures are taken. This study aims to produce a system for Predicting Heart Disease, which benefits to prevent and reduce the number of deaths caused by heart disease. The use of technology in the health sector has been widely practiced in various places and one of the advanced technologies is machine learning. Machine learning technology can be used to predict the potential patients of heart disease by implementing the K-Nearest Neighbors (KNN). The algorithm results in 65.93% for its accuracy, which is then improved to 82.41% due to the z-score normalization. It shows that z-score can noticeably improve the accuracy of the KNN algorithm. The system is developed based on a website that uses the Flask micro-framework so that development is more efficient. Flask is a micro-framework based on the Python programming language that does not contain many tools and libraries, so it is more portable and does not utilize a lot of resources.

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Publication Date
Sun Sep 03 2017
Journal Name
Baghdad Science Journal
Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor

There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task. In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that

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Publication Date
Tue Feb 05 2019
Journal Name
Journal Of Al-qadisiyah For Computer Science And Mathematics
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Publication Date
Fri Dec 30 2011
Journal Name
Al-kindy College Medical Journal
Postprandial Hyperglycemia as a Significant Risk Factor for Coronary Heart Disease

Background: The highest concentrations of
blood glucose during the day are usually found
postprandialy. Postprandial hyperglycemia (PPH)
is likely to promote or aggravate fasting
hyperglycemia. Evidence in recent years suggests
that PPH may play an important role in functional
& structural disturbances in different body organs
particularly the cardiovascular system.
Objective: To evaluate the effect of (PPH) as a
risk factor for coronary Heart disease in Type 2
diabetic patients.
Methods: Sixty-three type2 diabetic patients
were included in this study. All have controlled
fasting blood glucose, with HbA1c correlation.
They were all followed for five months period
(from May to October 2008)

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Publication Date
Mon Aug 01 2011
Journal Name
Journal Of Engineering
IMPLEMENTATION OF ROOT FINDING ALGORITHM OF MINIMUM PHASE FILTER USING VHDL

Root-finding is an oldest classical problem, which is still an important research topic, due to its impact on computational algebra and geometry. In communications systems, when the impulse response of the channel is minimum phase the state of equalization algorithm is reduced and the spectral efficiency will improved. To make the channel impulse response minimum phase the prefilter which is called minimum phase filter is used, the adaptation of the minimum phase filter need root finding algorithm. In this paper, the VHDL implementation of the root finding algorithm introduced by Clark and Hau is introduced.
VHDL program is used in the work, to find the roots of two channels and make them minimum phase, the obtained output results are

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Publication Date
Wed Jul 20 2022
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
A Scoping Review of Machine Learning Techniques and Their Utilisation in Predicting Heart Diseases

Heart diseases are diverse, common, and dangerous diseases that affect the heart's function. They appear as a result of genetic factors or unhealthy practices. Furthermore, they are the leading cause of mortalities in the world. Cardiovascular diseases seriously concern the health and activity of the heart by narrowing the arteries and reducing the amount of blood received by the heart, which leads to high blood pressure and high cholesterol. In addition, healthcare workers and physicians need intelligent technologies that help them analyze and predict based on patients’ data for early detection of heart diseases to find the appropriate treatment for them because these diseases appear on the patient without pain or noticeable symptoms,

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Publication Date
Tue Aug 31 2021
Journal Name
Iraqi Journal Of Science
A lightweight AES Algorithm Implementation for Secure IoT Environment

In recent years, the rapid development in the field of wireless technologies led to the appearance of a new topic, known as the Internet of things (IoT). The IoT applications can be found in various fields of our life, such as smart home, health care, smart building, and etc. In all these applications, the data collected from the real world are transmitted through the Internet; therefore, these data have become a target of many attacks and hackers. Hence, a secure communication must be provided to protect the transmitted data from unauthorized access. This paper focuses on designing a secure IoT system to protect the sensing data. In this system, the security is provided by the use of Lightweight AES encryption algorithm to encrypt the d

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Publication Date
Thu Jun 30 2016
Journal Name
Al-kindy College Medical Journal
Association between periodontitis and acquired coronary heart disease

Background: The association between oral microbial infection and systemic disease is not a new concept. A major confounding issue is that oral infections often are only one of the many important factors that can influence systemic diseases .Objective: This study was conducted to evaluate the periodontal health status of patients with acquired coronary heart disease. Type of the study: Cross-sectional study.Methods: The study group consisted of 200 patients with an age range (35-70) years, having coronary heart disease .This study group were compared to a control group of non-coronary heart disease (200 individuals ) matching with age and gender. The oral parameters were examined including the periodontal conditions, assessment of periodo

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Publication Date
Sun Oct 01 2017
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Pre-operative serum TSH level estimation for predicting malignant nodular thyroid disease

Background: the aim of this study was to assess the value of serum thyroid–stimulating hormone (TSH) levels in predicting malignancy in patients with nodular thyroid disease (NTD). Objective: The aim was to examine the relationship between preoperative TSH and differentiated thyroid cancer (DTC).
Patients and Method: all patients with NTD who were admitted in the first surgical unit of Baghdad teaching hospital and assessed for preoperative TSH level before subjecting them for thyroidectomy from first of April 2014 to 31 of January 2016, were included in the study. A preoperative database sheets including Age, gender, nodule size, and pathology were evaluated. Logistic regression analysis was used t

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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Journal Of Science
Heart Disease Classification–Based on the Best Machine Learning Model

    In recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne

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Publication Date
Sun Oct 02 2011
Journal Name
Journal Of The Faculty Of Medicine Baghdad
The role of HSP60 in Atherosclerotic Coronary Heart disease

Background: Heat shock proteins have a general role in the response of the arterial wall to stress and may serve as a mediator/inducer of atherosclerosis in particular circumstances when HSPs specifically bind to the Toll-like receptor 4/CD14 complex, initiating an innate immune response, including the production of pro-inflammatory cytokines, this also followed by cytokine amplification through transmigration of macrophages and neutrophils.
Objective: To investigate the percentage of expression of HSP60 by peripheral blood lymphocyte (PBL) in atherosclerotic coronary heart disease (CHD) patients using immunocytochemistry technique.
Method: A total of fifty patient (40 males and 10 females), ranged from the mean age (59.12±8.54) y

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