A method to predict diagnostic codes for chronic diseases using machine learning techniques

2016 ◽  
Author(s):  
Deepa Gupta ◽  
Sangita Khare ◽  
Ashish Aggarwal
Keyword(s):  
Machine Learning ◽  
Chronic Diseases ◽  
Machine Learning Techniques ◽  
Diagnostic Codes ◽  
Learning Techniques

Machine Learning Techniques for Predicting Outcomes of COVID-19 for Patients with preexisting Chronic Diseases

2021 ◽  
pp. 867-882
Author(s):  
Belmina Pramenković ◽  
Džejna Prasko ◽  
Evelina Pulo ◽  
Ines Rončević ◽  
Rasema Ramić ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Diseases ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Chronic Diseases System Based on Machine Learning Techniques

2020 ◽  
Vol 1 (1) ◽  
pp. 18-36
Author(s):  
Sahar A. EL_Rahman ◽  
Reem Ahmed AlRashed ◽  
Duna Nasser AlZunaytan ◽  
Nada Jahz AlHarbi ◽  
Shroog Abdullah AlThubaiti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Kidney Disease ◽  
Chronic Diseases ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Health Concern ◽  
Paper Machine ◽  
Learning Techniques

This paper aims to improve the quality of the patient's life and provide them with the lifestyle they need. And we have the intention to obtain this by creating a mobile application that analyzes the patient's data such as diabetes, blood pressure, and kidney. Then, implement the system to diagnose patients of chronic diseases using machine learning techniques such as classification. It's hard for the patients of chronic diseases to record their measurements on a paper every time they measure either the blood pressure or sugar level or any other disease that needs periodic measurements. The paper might be lost, and this can lead the doctor not fully to understand the case. So, the application is going to record measurements in the database. Also, it's difficult for patients to decide what to eat or how many times they should exercise according to their situation. Our idea is to recommend a lifestyle for the patient and make the doctor participate in it by writing notes. In this paper, machine learning classifiers were used to predict whether the person is prone to some chronic diseases. Blood pressure, diabetes and kidney are considered in this work. Orange3 from Anaconda-Navigator is the data mining tool used to test some machine learning algorithms. Blood pressure is the amount of force that blood exerts on the walls of the arteries as it flows through them. When this pressure reaches high levels, it can lead to serious health problems. For hypertension, Tree algorithm has shown 100% accuracy, which was the best one. Chronic Kidney Disease (CKD) is a significant public health concern with rising prevalence. With a set of considered attributes such as specific gravity, albumin, serum creatinine, hemoglobin, packed cell volume and hypertension used to predict if the person has Kidney disease or not. For kidney, Random Forest algorithm has shown 100% accuracy, which was the best one among other algorithms tested. Diabetes is a chronic disease when it cannot the pancreas to produce insulin, or when the body cannot use the insulin the pancreas produced. We considered attributes such as pregnancies, glucose, blood pressure, skin thickness, insulin, diabetes pedigree function, age and BMI of a person to diagnose whether a patient has diabetes based on specific diagnostic measurements or not. For diabetes, neural networks have shown the best accuracy. It was 76.3%.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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