Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naive Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor.

https://link.springer.com/content/pdf/10.1007/s42979-020-00365-y.pdf

Heart Disease Prediction using Machine Learning Techniques

Improving cyberbullying detection using Twitter users’ psychological features and machine learning

Heart disease causes a significant mortality rate around the world, and it has become a health threat for many people. Early prediction of heart disease may save many lives; detecting cardiovascular diseases like heart attacks, coronary artery diseases etc., is a critical challenge by the regular clinical data analysis. Machine learning (ML) can bring an effective solution for decision making and accurate predictions. The medical industry is showing enormous development in using machine learning techniques. In the proposed work, a novel machine learning approach is proposed to predict heart disease. The proposed study used the Cleveland heart disease dataset, and data mining techniques such as regression and classification are used. Machine learning techniques Random Forest and Decision Tree are applied. The novel technique of the machine learning model is designed. In implementation, 3 machine learning algorithms are used, they are 1. Random Forest, 2. Decision Tree and 3. Hybrid model (Hybrid of random forest and decision tree). Experimental results show an accuracy level of 88.7% through the heart disease prediction model with the hybrid model. The interface is designed to get the user's input parameter to predict the heart disease, for which we used a hybrid model of Decision Tree and Random Forest.

Heart Disease Prediction using Hybrid machine Learning Model

Diseases related to Heart i.e. Cardiovascular Dis-eases (CVDs) are the main reason for the number of deaths in the course of the most recent couple of decades and has developed as the most perilous ailment, in India and in the entire world. In this way, there is a need for accurate, feasible and reliable system to analyze such maladies in time for legitimate treatment.  Machine Learning algorithms and procedures have been im-plemented to various medical datasets to various medical datasets to investigate of extensive and complex information. Numerous analysts, as of late, have been using several methods to enable the health care industry and the professionals in the diagnosis of heart related diseases. This paper demonstrates a survey of various models based on such algorithms and techniques and analyze their perfor-mance. Models depend on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Naive Bayes, Decision Trees (DT), Random Forest (RF) and ensemble models are discovered extremely prominent among the researchers.

Role of machine learning in medical research: A survey

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

/pdf/heart-disease-prediction-using-machine-learning-techniques-a-2q4ijgjtsz.pdf

Heart disease prediction using machine learning techniques : a survey

Essays are paramount for of assessing the academic excellence along with linking the different ideas with the ability to recall but are notably time consuming when they are assessed manually. Manual grading takes significant amount of evaluator's time and hence it is an expensive process. Automated grading if proven effective will not only reduce the time for assessment but comparing it with human scores will also make the score realistic. The project aims to develop an automated essay assessment system by use of machine learning techniques by classifying a corpus of textual entities into small number of discrete categories, corresponding to possible grades. Linear regression technique will be utilized for training the model along with making the use of various other classifications and clustering techniques. We intend to train classifiers on the training set, make it go through the downloaded dataset, and then measure performance our dataset by comparing the obtained values with the dataset values. We have implemented our model using java.

https://iopscience.iop.org/article/10.1088/1742-6596/1000/1/012030/pdf

Automated Essay Grading using Machine Learning Algorithm

This paper presents a novel method based on concept of Machine Learning for Emotion Detection using various algorithms of Support Vector Machine and major emotions described are linked to the Word-Net for enhanced accuracy. The approach proposed plays a promising role to augment the Artificial Intelligence in the near future and could be vital in optimization of Human-Machine Interface.

https://iopscience.iop.org/article/10.1088/1742-6596/1000/1/012027/pdf

Emotion detection from text

Objectives: To training classifier based on the features extracted from the poems of Mukkoodar Pallu, authors for various unknown poems can be classified. Methods/Analysis: The classification accuracy by performing classification in the dataset using C4.5 algorithm is illustrated in this paper. Findings: The results of performing classification on dataset that consists of features extracted from the dataset are shown in this paper. Features like number of characters, number of sentences and the classification accuracy when C4.5 algorithm is used is illustrated. Novelty/Improvement: By doing this, authors of various other poems in Tamil language can be identified which will be helpful to the society. Also a generalized authorship identification tool for all regional languages can be achieved.

/pdf/authorship-identification-for-tamil-classical-poem-mukkoodar-32wyhky9p4.pdf

Authorship Identification for Tamil Classical Poem (Mukkoodar Pallu) using C4.5 Algorithm

Background/Objectives: The main objective of the present study is to do the prerequisite process to develop a viewerfriendly electronic embedded system and business beneficial system to promote their products. This can be achieved by classifying the extracted Advertisement (ADD) videos from the Non-Advertisement (NADD) videos which consists of more visual information. Methods/ Statistical Analysis: The proposed frame work facilitates to identify the advertisement and non advertisement videos from the live stream television videos are discussed. The Block Intensity Comparison Code (BICC) technique is applied to extract the essential features from the ADD and NADD video frames. The frames are divided into various block sizes to select the best performing block size of the frame. The 8x8 frame size has been chosen as the promising block size to conduct the experiments. An extensive experimental analysis has been demonstrated with different classifier and a comparative study also reported. Findings: Decision tree algorithm (C4.5) has been employed to identify the vibrant features and these features are taken as the input to the various decision tree algorithms, namely J48, J48graft, LM tree, Random tree, BF tree, REP tree and NB tree to classify the video genre. A broad investigation has been made by a random tree algorithm which produced better predictive performance than the other algorithms. The training and the optimization of random tree model with their essential parametric measures are reported. Based on the overall study, random tree with BICC feature was found as the most preferred classification algorithm that achieved the 92.08% than the other algorithms. The classification capability and the performance evaluation of random tree algorithm with block intensity comparison code is reported and discussed for further study. Application/Improvements: The performance of the classifier can also be improved with other novel features.

V. V. Ramalingam

Papers

Heart disease prediction using machine learning techniques : a survey

Automated Essay Grading using Machine Learning Algorithm

Emotion detection from text

Authorship Identification for Tamil Classical Poem (Mukkoodar Pallu) using C4.5 Algorithm

Performance Comparison of Various Decision Tree Algorithms for Classification of Advertisement and Non Advertisement Videos