Online ISSN: 2515-8260

Keywords : Decision Tree


SURVEY ON VARIOUS PREDICTION MODELS FOR SURVIVAL OF BREAST CANCER PATIENTS USING WARM BOOT RANDOM FOREST CLASSIFIER

Vibin chandar; Dr. Krishnapriya . V

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 856-865

The rapid growth of genomics and proteomics in science has led to the
exponential development of information that requires a complex computational analysis to
find details. Review of statistical science or bioinformatics using knowledge mining centres
using bioinformatics to resolve a range of certifiable problems in the field of medical services.
Breast cancer malignant growth is the second most deadly form of disease that causes a
woman to die. Numerous experts have led to the early detection, visualisation and improved
management of malignancy in the breast cancer over the last 20 years, contributing to a
reduction in the rate of death. However the problem of malignancy in the breast
cancer remains concerning and requires further study in the territory of the development of
locations and forecasts other than treatment methods. This article explore the present
situation with the technique of estimating breast cancer disease status, which includes the
study on breast cancer malignancy, breast cancer, the prediction of the risk of malignant
growth, and the prediction of survival for breast cancer disease.

Exploration Of A State Of The Art On Cardiac Diseases Prediction Techniques

S. Usha; Dr.S. Kanchana

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 7, Pages 6962-6967

Healthcare is a predictable task to wipe out human life. Coronary heart disease is sickness that impacts the human coronary heart. Cardiovascular sicknesses will forecast with the aid of several techniques that helped in making choices about the modifications that maintain excessive-risk patients which resulted in the discount of their dangers. The purpose of demise ratio of those sicknesses may be very high. It is very imperative to become aware of if the individual has heart disorder or now not. In medical field it is very important to find the occurrence of prediction of the heart diseases. Accurate Prediction results are very efficient to treat the patient’s medical history before the attack occurs. The techniques Data mining and Machine learning plays a essential role to predict the occurrence of heart diseases. These techniques diagnose these diseases with the help of dataset in healthcare centers. Various models used to reduce the number of deaths ratio. Models based on several algorithms such as Support Vector Machine (SVM), Decision Tree(DT), Naïve Bayes(NB), K-Nearest Neighbor(KNN), and Artificial Neural Network (ANN) are implemented to predict heart disease. The accuracy of these models helps to diagnose the diseases with better results. This paper summarized the performance of all algorithms which are used to predict and diagnose heart diseases.

Design And Development Of An Augmented Reality Application To Learn Mandarin

Zaidatol Haslinda ABDULLAH SANI; Seng HUIYI; Teoh Shun HONG; Dinna N. MOHD NIZAM; Aslina BAHARUM

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 8, Pages 3814-3826

This paper presents the design and development of an augmented reality (AR) app to enhance learning Mandarin among university students using a user-centered design life cycle (UCDL). A survey was conducted to investigate the difficulty of learning Mandarin and the thoughts of using technology to assist the students in learning the language. Forty-five students participated in the survey. The results show that participants have difficulty learning to speak, write, read, or listen in Mandarin, with writing was found to be the most difficult (M = 3.49, SD = .94). The majority of the participants (n = 39, 87%) reported having never seen or used an AR education app. However, most (n = 36, 80%) also said that they are interested in using an AR app to learn Mandarin. A low-fidelity prototype of an AR app to assist students in learning Mandarin was designed. An expert usability evaluation was conducted with three experts. Thirty-three usability problems were found, and further changes to the low-fi were designed. A usability evaluation of the low-fi with a group of students will be conducted followed by the app’s development. A final round of usability testing of the final app will also be conducted.

Analyzing Diabetic Data Using Naive-Bayes Classifier

A. Sharmila Agnal; E. Saraswathi

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2687-2699

Approximately 422 million people across the world have diabetes, particularly in countries where the average income is in the middle and lower end of the economic spectrum. Statistics reveal that every year, about 1.6 million deaths are recorded which can be directly attributed to diabetes. The graph suggests that number of cases as well as the prevalence of diabetes have been steadily incrementing over the past few decades. Through this new implementation of the Bayesian Classifier, raw medical data is analyzed and the risk of diabetes diagnosis based on each patient’s medical information can be calculated. The raw data is converted into class labels and the likelihood of a positive potential diabetes case is derived, as a probability (≤1). This can not only be used by healthcare professionals but also by common users, and can be useful in detecting the risk and preventing it in time without taking any medical tests. This classifier uses very basic information that would be known to each patient or can easily be obtained.

MACHINE LEARNING TECHNIQUE BASED PARKINSON’S DISEASE DETECTION FROM SPIRAL AND VOICE INPUTS

Jaichandran R; Leelavathy S; Usha Kiruthika S; Goutham Krishna; Mevin John Mathew; Jomon Baiju

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2815-2820

Parkinson’s disease is a dynamic neurodegenerative disorder influencing over 6 million people
worldwide. However there is no recognized test for PD for patients, particularly in the early stages. This
results in increased mortality rate. Thus detection system of Parkinson’s disease with easy steps and
feasible one to detect parkinson’s disease at the early stage is essential. The proposed system invokes
parkinson’s disease detection using voice and spiral drawing dataset. The patients voice dataset is
analyzed using RStudio with kmeans clustering and decision tree based machine learning techniques.
The patients spiral drawing is analyzed using python. From these drawings principal component analysis
(PCA) algorithm for feature extraction from the spiral drawings. From the spiral drawings : X ; Y; Z;
Pressure; Grip Angle; Timestamp; Test ID values are been extracted. The extracted values are been
matched with the trained database using machine learning technique (Support vector machine) and
results are produced. Thus our experimental results will show early detection of disease which facilitates
clinical monitoring of elderly people and increase their life span by improving their lifestyle which leads
to a peaceful life.

Implementation Of Statistical Learning Model For Room Occupancy Detection

Raja Fazliza Raja Suleiman; Muhammad Iqbal Nebil

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 8, Pages 3737-3746

This paper presents several room occupancy detection methods using statistical learning model. Occupancy detection system is mainly used for energy saving in green buildings such as offices and residential apartments. The system will automatically switched-off the lighting, heating or ventilation appliances when the room is empty. The proposed work uses temperature and humidity sensor to detect human presence. All the input values from this sensor are transmitted to an IoT platform called Blynk (for data monitoring), through the medium of an open-source microcontroller board NodeMCU. The collected data is analyzed using two different approaches which are supervised learning model and unsupervised learning model. Results show that for supervised learning, SVM performs slightly better than Decision Tree. While for unsupervised subspace learning, Minimax yields better probability of detection than SVD in worst case criterion.

Multisensor Data Fusion Technique For Environmental Awareness In Wireless Sensor Networks

Reyana A; Vijayalakshmi P

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 8, Pages 4479-4490

Sensing in forest area is the most widely used application concerning investigation studies
of climate change. Wireless sensor networks with spatially scattered sensors enable the
application to record climate change disturbance detecting condition changes in
temperature, humidity, sound, wind, etc. The highly automated method herein can pass the
observed information bi-directionally to the system sink, empowering sensor activity
control. It is common knowledge that a multisensor environment has hundreds or
thousands of sensor nodes connected. With recent innovations, the remarkable challenge
faced in a multisensor climate is to rapidly acquire specific information from a reliable
route exhibiting high data accuracy. This proposed ADKF-DT-MF algorithm for
multisensor data fusion combines sensor information in-continuous time, providing a
rapid information exchange on climate change for environmental awareness. The findings
significantly show a better RMSE of 0.85 than the previous results reported in the
literature MHT-EnKF. The quality of estimation was explored, calculating the best costs,
ensuring an increase in data fusion accuracy for active awareness. The tests on simulated
data applying fuzzy membership optimization function show improvement in the ADKFDT-
MF multisensor fusion system's performance.

Crop Value Forecasting using Decision Tree Regressor and Model s

AkshayPrassanna S; B A Harshanand, B Srishti; Chaitanya R; KirubakaranNithiyaSoundari .; SwathiSriram .; V Manoj Kumar; VarshithaChennamsetti .; Venkateshwaran G; Dr.Pramod Kumar Maurya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3702-32722

Machine Learning is an emerging research field which can be used for the analysis of crop
price prediction and accurately provide solutions for the same. We can use this system as a backhand
while we decide what a farmer should plant while considering factors such as annual rainfall, WPI
and so on which is provided from the dataset and produce a logical conclusion on which products
would give a more reliable outcome. The performance between Random forest ensemble learning and
decision tree regressor is compared and it has been observed that the Random Forest Ensemble
learning method gives a higher accuracy. In this system there are 23 crops whose information can be
accessed upon for deciding collaborated with a simple user friendly UI

Symptoms Based Disease Prediction Using Decision Tree and Electronic Health Record Analysis

S Radhika; S Ramiya Shree; V Rukhmani Divyadharsini; A Ranjitha

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2060-2066

In this paper, we seek to predict user’s diseases based on their symptoms. To achieve our target, we use the Decision Tree Classifier which helps to detect the patient’s health condition after receiving their symptoms by giving the predicted disease. The dataset contains physiological measurements with 40 instances(Diseases) and 132 attributes(Symptoms). Additionally, the respective patient’s EHR is also collected for summarizing the prescription/test report using NLTK.