• International Journal of Advanced Culture Technology
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• 제8권4호
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• pp.167-176
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• 2020

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient's feedback with their information regarding their treatment. Patient's preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient's feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor's in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients' health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.177-184
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• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.1-7
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• 2020

As the demand for health screening increases, there is a need for efficient design of screening items. We build machine learning models for health screening and recommend screening items to provide personalized health care service. When offline, a synthetic data set is generated based on guidelines and clinical results from institutions, and a machine learning model for each screening item is generated. When online, the recommendation server provides a recommendation list of screening items in real time using the customer's health condition and machine learning models. As a result of the performance analysis, the accuracy of the learning model was close to 100%, and server response time was less than 1 second to serve 1,000 users simultaneously. This paper provides an adaptive and automatic recommendation in response to changes in the new screening environment.

• 한국융합학회논문지
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• 제11권11호
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• pp.19-24
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• 2020

최근, 애완동물의 급격한 증가로 애완동물의 건강상태 체크와 다양하게 수집된 데이터를 활용하여 사료 추천 등 통합적인 애완동물관련 개인화 상품 추천 서비스가 요구된다. 본 논문은 빅 데이터 기술을 활용하여 애완동물관련 데이터 수집, 전처리, 분석, 관리등 다양한 개인화서비스를 할 수 있는 상품 추천시스템을 구현한다. 먼저, 애완동물이 착용하고 있는 센서 정보와 고객의 구매 패턴, SNS 정보를 수집해 데이터베이스에 저장하고 통계적 분석을 활용하여 사료제작, 애완동물 건강관리 등 맞춤형 개인화 추천 서비스가 가능한 플랫폼을 구현한다. 본 플랫폼은 유사도가 분석될 상품과 상품정보에 대한 유사도 상품 정보를 출력하고 최종적으로 추천 분석한 결과를 출력하여 고객에게 정보를 제공 할 수 있다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.316-318
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• 2019

최근 고령화사회의 진입으로 건강수명이 이슈가 되고 있으며 삶의 질 향상을 위한 지속적 건강관리에 관심이 높아지고 있다. 본 논문에서는 사용자들의 편리한 건강관리를 위한 사용자 맞춤형 건강정보 추천 앱 시스템을 구현하였다. 사용자는 생활습관, 질병, 신체조건 등의 기본 정보를 입력하고 입력된 사용자의 PHR(Personal Health Record)는 서버에 저장된다. 저장된 다수의 사용자들을 PHR프로파일에 따라 유사한 군집으로 분류하여 유사 사용자들에게 헬스케어 관련 콘텐츠를 제공하고자 하였다. 사용자의 PHR에 따른 유사군집의 생성을 위하여 K-Means 클러스터링을 적용하였으며 지식베이스에 저장된 건강정보 콘텐츠들을 맞춤형으로 제공하기 위하여 개미군집 알고리즘을 사용하였다. 개발된 앱은 사용자의 PHR 프로파일로 분류된 군집에 따라 위험한 질병, 개선해야 할 생활 습관 등에 대한 정보를 제공하여 사용자의 자가 헬스케어에 활용될 수 있다.

• Journal of Electrical Engineering and Technology
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• 제10권6호
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• pp.2434-2441
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• 2015

This paper proposes an exercise recommendation system for treating obesity that provides systematic recommendations for exercise and diet. Five body indices are considered as indicators for recommend exercise and diet. The system also informs users of prohibited foods using health data including blood pressure, blood sugar, and total cholesterol. To maximize the utility of the system, it displays recommendations for both indoor and outdoor activities. The system is equipped with multimode sensors, including a three-axis accelerometer, a laser, a pressure sensor, and a wrist-mounted sensor. To demonstrate the effectiveness of the system, field tests are carried out with three participants over 20 days, which show that the proposed system is effective in treating obesity.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.600-602
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• 2015

WHO (World Health Organization)에서 세계적인 전염병이라고 지정한 비만으로 인해 국민 건강과 관련된 사회적 비용이 점차 증가하고 있다. 소득의 향상으로 인해 복지와 Wellbeing 분야에 대한 관심이 증가함에 따라 기존 의료분야의 연구목표가 질병을 치료하는 것에 있다면, 점차 미리 예방하고 관리하는 것으로 변화하고 있다. 본 논문에서는 이러한 사회적 변화를 고려하여 맞춤형 휘트니스 추천 시스템을 제안한다. 이는 사용자별로 어떤 운동 기구를 이용하여 운동을 하는 것이 효율적인지 추천해준다. 이를 위해 모바일 센서가 가지는 하드웨어적 한계점을 소프트웨어적으로 극복하고, 최적화된 센서 제어 메커니즘을 제시한다.

• 한국IT서비스학회지
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• 제20권6호
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• pp.1-16
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• 2021

The health functional food market continues to grow, and according to that trend, the subdivision sales of personalized health functional foods, which have been legally prohibited, will be operated as a special regulatory pilot project. Personalized health functional food recommendations have a variety of personalized indicators to consider, and it is believed that algorithmic methods will be needed to proceed in a customized manner considering all of them. This study aims to contribute to the development of the AI-based health functional food recommendation service by studying factors that affect the use of the AI-based health functional food recommendation service. This paper analyzed the intention of use for AI-based health functional food recommendation service based on the information system success model and Technology Acceptance Model. This study considered information quality factors, service quality factor, and system quality factor as independent variables influencing perceived usefulness, perceived ease of use and trust. For empirical analysis, 406 questionnaires were used and the collected data were performed using AMOS 22.0 and SPSS 22.0. Research has shown that the accuracy, timeliness, empathy and availability have a positive effect on usefulness. Understandability and availability has been shown to have a positive effect on ease of use. The accuracy, understandability, empathy and availibility has been shown to have a positive impact on Trust. Usefulness, ease of use and trust all have been shown to have a positive influence on intention of use.

• International journal of advanced smart convergence
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• 제9권3호
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• pp.145-152
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• 2020

When conducting a health screening, it is important to select the most appropriate hospitals for the screening items. There are various packages in the screening hospitals, and the screening items and price are very different for each package. In this paper, we provide a method of recommending the screening packages in consideration of the customer's preferences such as screening items and minimum matching ratio. First, after collecting package information of hospitals, information such as basic items and optional items in the package are extracted. Then, we determine whether the client's screening items exist in the basic item or optional item of the package and calculate the matching rate of the package. Finally, we recommend screening packages with the lowest price while meeting the minimum matching rate suggested by the client. For performance analysis, we implement a prototype for recommending screening packages and provide the experimental results. The performance analysis shows that the proposed approach provides a real-time response time and recommends appropriate packages.

• 한국컴퓨터정보학회논문지
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• 제26권6호
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• pp.19-28
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• 2021

코로나로 인해 건강관리에 대한 관심이 증가하고 있는 요즘, 여러 사람이 함께 이용하는 헬스장이나 공용시설을 이용하는데 어려움이 늘어남에 따라 홈 트레이닝을 하는 이들이 늘어나고 있다. 이에 본 연구에서는 홈 트레이닝 사용자들에게 좀 더 정확하고 의미 있는 운동 추천을 제공하기 위해 개인 성향 정보를 활용한 개인화된 운동 추천 알고리즘을 제안한다. 이를 위해 식습관 정보, 육체적 조건 등 개인을 나타낼 수 있는 개인 성향 정보를 사용해 k-최근접 이웃 알고리즘으로 데이터를 비만의 기준에 따라 분류하였다. 또한, 운동 데이터 셋을 운동의 레벨에 따라 등급을 구별하였으며 각 데이터 셋의 이웃 정보를 바탕으로 모델 기반 협업 필터링 방법 중 차원 축소모델인 특이값 분해 알고리즘(SVD)을 통해 사용자들에게 개인화된 운동 추천을 제공한다. 따라서 메모리 기반 협업 필터링 추천 기법의 데이터 희소성과 확장성의 문제를 해결할 수 있고, 실험을 통해 본 연구에서 제안하는 알고리즘의 정확도와 성능을 검증한다.