Patient Diet Recommendation System Using K Clique and Deep learning Classifiers

人工智能 机器学习 深度学习 计算机科学 朴素贝叶斯分类器 分类器(UML) 推荐系统 逻辑回归 特征工程 支持向量机
作者
S. Manoharan,Sathish
出处
期刊:Journal of Artificial Intelligence and Copsule Networks [Inventive Research Organization]
卷期号:2 (2): 121-130 被引量:47
标识
DOI:10.36548/jaicn.2020.2.005
摘要

There are several systems designed for the purpose of recommending. The recommending system has gained its prominence even in the medical industry for suggesting the diets for the patient’s, medicines to be taken, treatments to be taken etc. The recommendation system mainly enhances the robustness, extends protection against the many disease and improves the quality of living of an individual. So to automatically suggest the foods based on their health conditions and the level of sugar, blood pressure, protein, fat, cholesterol, age etc. the paper puts forth k-clique embedded deep learning classifier recommendation system for suggesting the diets for the patients. The K-clique incorporated in the recommendation system in an effort of getting an improved preciseness and increasing the accuracy of the deep learning classifier (gated recurrent units). The dataset for the empirical analysis of the developed system was performed with the data set of the patients collected over the internet as well as hospitals, information’s of about 50 patients were collected with thirteen features of various disease and thousand products with eight feature set. All these features were encoded and grouped into several clusters before applying into the deep learning classifiers. The better preciseness and the accuracy observed for the developed system experimentally is compared with the machine learning techniques such as logistic regression and Naïve Bayes and other deep learning classifiers such as the MLP and RNN to demonstrate the proficiency of the K-clique deep learning classifier based recommendation system (K-DLRS)
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
共享精神应助杨晗庆采纳,获得10
刚刚
orixero应助踏实青槐采纳,获得10
刚刚
1秒前
默默访风发布了新的文献求助10
2秒前
mendicant发布了新的文献求助30
2秒前
2秒前
潇洒的如松关注了科研通微信公众号
3秒前
3秒前
SXM发布了新的文献求助10
3秒前
lyh发布了新的文献求助10
4秒前
Laurels完成签到,获得积分10
4秒前
llzuo发布了新的文献求助10
4秒前
希望天下0贩的0应助WHAN采纳,获得10
5秒前
kiki完成签到,获得积分10
5秒前
Jasper应助emily采纳,获得10
6秒前
小垃圾10号完成签到,获得积分10
6秒前
JamesPei应助zzn采纳,获得10
6秒前
彩色的若颜完成签到,获得积分10
6秒前
6秒前
我是老大应助蔺山河采纳,获得10
6秒前
Laurels发布了新的文献求助10
7秒前
MOON发布了新的文献求助10
8秒前
11秒前
舒服的小懒虫完成签到,获得积分10
11秒前
诚心初晴发布了新的文献求助20
12秒前
SciGPT应助诗恋菲宇采纳,获得10
12秒前
12秒前
12秒前
许你人间一两风完成签到,获得积分20
12秒前
sdada发布了新的文献求助10
14秒前
TK发布了新的文献求助10
14秒前
14秒前
14秒前
15秒前
15秒前
16秒前
Arrhenius完成签到,获得积分10
16秒前
16秒前
高分求助中
Sustainable Land Management: Strategies to Cope with the Marginalisation of Agriculture 1000
Corrosion and Oxygen Control 600
Yaws' Handbook of Antoine coefficients for vapor pressure 500
Python Programming for Linguistics and Digital Humanities: Applications for Text-Focused Fields 500
行動データの計算論モデリング 強化学習モデルを例として 500
Johann Gottlieb Fichte: Die späten wissenschaftlichen Vorlesungen / IV,1: ›Transzendentale Logik I (1812)‹ 400
The role of families in providing long term care to the frail and chronically ill elderly living in the community 380
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2554352
求助须知:如何正确求助?哪些是违规求助? 2179140
关于积分的说明 5617778
捐赠科研通 1900298
什么是DOI,文献DOI怎么找? 948944
版权声明 565556
科研通“疑难数据库(出版商)”最低求助积分说明 504531