Heart Disease Prediction using Machine Learning Techniques

--Heart disease is one of the notable causes of death in worldwide everyday divination of heart disease is a critical challenge in area of medical data analysis. Machine learning (ML) has been shown to be successful in helping in making decision and prediction from huge quantity of data produced by medical health care industry. Medical data mining is an important area of data mining and considered as one of the important research field due to the application in medical health care domain. In medical data mining classification and prediction of medical dataset process challenges. It is difficult to medical practice to predict the heart attack as it complex task. The health sector at present contain the information that are hidden and which are important in making decision. Data mining algorithms such as Naive Bayes algorithm, decision tree algorithm and random forest are applied in the research for heart disease prediction the result show the comparison between the three algorithm and selecting the best one among three, the Random forest algorithm will provide the more accuracy can compared to all the three algorithms.

Skin disease is a very vulnerable and severe issue in today's world. Skin disorder classification is crucial for diagnosis. Several new data mining algorithms have been developed to classify and interpret medical images. The functionality of the K-Nearest Neighbors (KNN) Random Forest (RF) Algorithm is described in this article, along with an analysis of its results. Furthermore, this study demonstrates a high-performing approach that saves both effort and money. The proposed model is designed based on KNN and the Random Forest algorithm. The patient can use this model to classify his skin disease as a primary detection, and the doctor also can ensure his judgment by using this proposed model. Traditional skin disease diagnosis is an expensive and time-consuming procedure. This paper's proposed classification model will identify ten different skin diseases. The Random Forest algorithm has a testing accuracy of 94.22 percent, and K-Nearest Neighbors (KNN) has a testing accuracy of 95.23 percent. The KNN algorithm has an F1 Score of 95.98 percent, whereas the Random Forest (RF) algorithm has an F1 Score of 95.94 percent. It can be increased by expanding the dataset and more feature extraction. This approach may benefit individuals with skin illness who are looking to save money and time as well as avoid skin cancer by identifying cancer at an early stage.

Aim: Coronary illness is one of the main sources of mortality in this current world. A basic test in the field of clinical information assessment is the expectation of cardiovascular infection. The fundamental target of this paper is to work on the exactness of the Data mining algorithms being used to improve heart disease prediction. We will be comparing the novel Random forest with K Nearest Neighbor to find out which of these can give us the best accuracy. Material and Methods: The study used 98 samples with Novel Random Forest and K Nearest Neighbor is executed with varying training and testing splits for predicting the accuracy for heart disease prediction with the 80% of G-power value and data about cardiovascular disease was gathered from numerous web sources, including latest research findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the accuracy rate of the classifiers is examined using the heart disease dataset.. There was a statistical insignificant difference among Innovative Random Forest and K Nearest Neighbor p = 0.484 (p>0.05). Results and Discussion: The accuracy of predicting heart disease in Novel Random Forest 90.16% and K Nearest Neighbor 67.21% is obtained. Conclusion: Prediction of Heart disease using the innovative Random Forest (RF) technique appears to be a significant improvement over the K Nearest Neighbor (KNN) algorithm in terms of accuracy.

The increasing number of students dropping out is a major concern of higher educational institutions as it gives a great impact not only cost to the students but also a waste of public funds. Thus, it is imperative to understand which students are at risk of dropping out and what are the factors that contribute to higher dropout rates. This can be done using educational data mining. In this paper, we described the uses of data mining techniques to predict student dropout of Computer Science undergraduate students after 3 years of enrolment in Universiti Teknologi MARA. The experimental results showed an achievable reliable classification accuracy from the selected algorithm in predicting dropouts. Decision tree, logistic regression, random forest, K-nearest neighbour and neural network algorithm were compared to propose the best model. The results showed that some of the machines learning algorithms are able to establish effective predictive models from student retention data. The Logistic Regression model was found to be the best learners to predict the dropout students with identified potential subject causes. In addition, we also presented some findings related to data exploration.

Energy management of hybrid energy system sources based on machine learning classification algorithms

Introduction: Prostate cancer is one of the leading causes of death in men, and the early detection of this disease can be a significant factor in controlling and managing it. Applying data mining techniques can lead to the extraction of hidden knowledge from a huge amount of data and can help diagnose this disease by physicians. This study aims to determine the algorithm with the best performance to diagnose prostate cancer.Methods: In this study, nine data mining techniques, including Support Vector Machine, Decision Tree, Naive Bayes, K-Nearest Neighbors, Neural Network, Random Forest, Deep Learning, Auto-MLP, and Rule Induction algorithms, were used to extract hidden patterns from prostate cancer data. In this study, the data of 100 patients, which included eight characteristics, were used, and the RapidMiner Studio environment was employed for modeling. To compare the performance of the mentioned approaches used in this study to diagnose prostate cancer, accuracy, recall, precision, AUC, sensitivity, and specificity were calculated and reported for all techniques.Results: The results of this study showed that the accuracy of the applied algorithms was between 77% and 84%. Using different criteria to evaluate the techniques used showed that the two algorithms K-Nearest Neighbors and Neural Network, had better performance and accuracy (84%) than other methods. The sensitivity in these two algorithms was 80% for Neural Networks and 85% for K-Nearest Neighbors, respectively.Conclusion: The usage of different data mining techniques can lead to the discovery of hidden patterns among an enormous amount of data related to prostate cancer, and as a result, it leads to the early diagnosis of this disease and saves the subsequent costs.

Basic science: Pathophysiology: the CardioRenal Metabolic Syndrome

Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.

In the current scenario, plant disease detection is seeking attention from many agricultural scientists. Plant diseases are deeply influenced by the weather conditions, and each disease has its individual weather requirements. The changes in weather parameters such as humidity, temperature, wind speed, etc., can cause many diseases in tomato plants. In the current empirical study, we have taken specific disease powdery mildew whose fungus is named as Leveillula Taurica which belongs to Leotiomycetes class, and it is responsible for the occurrence of this specific disease in tomatoes. In this research, three weather-based prediction models have been developed using k-nearest neighbor (kNN), decision tree (DT), and random forest (RF) algorithm for powdery mildew disease prediction in tomatoes at an early stage. Results indicate that the proposed model, based on RF algorithm, shows the best accuracy of 93.24% for tomato powdery mildew disease (TPMD) dataset. A real-time version of the proposed model can be used by the agricultural experts to take preventive measures in the most sensitive areas that are prone to powdery mildew disease based on the weather conditions. Hence, timely intervention would help in reducing the loss in productivity of tomato crops which will further benefit the global economy, agricultural production, and the food industry.

To examine the associations between physical activity and a wide range of risk factors for ischaemic heart disease including fibrinogen concentration and viscosity. Cross sectional evidence from the 2398 men aged 50-64 years in the Caerphilly Prospective Heart Disease Study. Validated questionnaires were used to quantify energy expenditure on leisure activities and to grade activities related to occupation. Risk factors for heart disease examined included blood pressure, lipids, fibrinogen, and plasma viscosity. Possible confounding variables included smoking, employment, and prevalent heart disease (angina, previous myocardial infarction, and electrocardiographic evidence of ischaemia). Fibrinogen concentration was lower by 0.24 g/l and viscosity by 0.026 cP in the third of men who were most active in leisure activities (about 0.25 x 1 SD). A weak positive relation was found with high density lipoprotein cholesterol, but none with total cholesterol or fasting glucose concentrations or blood pressure. Triglyceride concentrations seem to be substantially lower in the most active men, although the evidence for this is not consistent. Work related activity showed relation with the lipid concentration but not with the haemostatic tests. Leisure activities of all levels seem to affect haemostatic and lipid factors beneficially. These effects correspond to a difference in the risk of heart disease for an active man and a sedentary man of at least 7% or 8%. Fasting triglyceride concentrations have already been shown to be strongly predictive of heart disease in this cohort of men, and the effect of exercise on this factor is also likely to confer benefit.

heart disease is a dead-list disease in the world. Unfortunately, people around the world have been facing heart diseases. Some heart disease conditions are silently grown up without having any symptoms. Usually, doctors make this prediction by manually checking medical test result reports and patients' symptoms. Sometimes, this traditional prediction might be right or wrong. Any technology that can help to diagnose heart disease before much damage will be supported to protect human lives and the accuracy of the result will be higher than the traditional prediction. Machine learning is the latest technology that is used to predict early diseases. This research's main purpose is to introduce a mobile application using a machine learning model to predict heart disease stages (no risk, low risk, risk, high risk, and higher risk) by using eight features of the UCI heart disease dataset and provide an in-depth look at machine learning techniques for data cleaning, data sampling, data splitting, and machine learning models. Random forest, decision tree, and k-nearest neighbors algorithms are the main machine learning algorithms that are used in this research and 85% is the highest accuracy level that is obtained by machine learning models.

As the most widely known data mining algorithm, classification algorithms have attracted wide attention. K-Nearest Neighbor (KNN) algorithm and decision tree algorithm are the two widely known algorithms in classification algorithms. Sometimes, people not sure how to choose the suitable to solve the classification problems. In this paper, we establish KNN algorithm model and decision tree ID3 algorithm model to analyze the accuracy of the two algorithms in the same data set with different number of features. Through the learning curve and cross validation, we find ID3 algorithm is better than KNN algorithm, and when the number of feature increased the accuracy of KNN is increasing while ID3 is decreased.

ABSTRAK Plak ateroklerosis menyebabkan penyakit jantung koroner, yang menjadi penyebab kematian kedua di negara berkembang. Penyakit jantung koroner menyebabkan 17,9 juta kematian pada tahun 2016—31% dari semua kematian di seluruh dunia. Lebih dari tiga perempat kasus kematian di negara berkembangan disebabkan oleh penyakit jantung dan pembuluh darah. Aktivitas fisik yang berlebihan, stres, udara yang dingin, pola hidup yang kurang sehat, dan riwayat penyakit jantung dari keluarga adalah faktor risiko penyakit jantung koroner. Risiko penyakit jantung koroner meningkat pada penderita yang memiliki banyak faktor risiko penyakit jantung. Oleh karena itu, individu dengan faktor risiko ini disarankan melakukan deteksi dini dengan pemeriksaan rekam jantung secara berkala untuk mencegah dan apabila ditemukan gangguan irama jantung, penderita dapat melakukan pemeriksaan lanjutan berdasarkan gejala klinis dan hasil rekam jantung. Kegiatan pengabdian bertujuan untuk deteksi dini penyakit jantung koroner melalui pemeriksaan rekam jantung pada sivitas akademika Universitas Khairun. Metode yang dilakukan adalah pemeriksaan rekam jantung dan melakukan tanya jawab terkait keluhan yang dirasakan saat diperiksa. Hasil yang didapatkan dari kegiatan ini tedapat 14 orang yang memiliki hasil rekam jantung yang bervariasi. Dari kegiatan perlu adanya pemantauan berkala terhadap risiko penyakit jantung koroner agar dapat mencegah terjadinya penyakit jantung dan meningkatkan kualitas kerja sivitas akademika Universitas Khairun. Kata Kunci: Deteksi Dini, Penyakit Jantung Koroner, Pemeriksaan Rekam Jantung ABSTRACT Plaque atherosclerosis causes coronary heart disease, which is the second leading cause of death in developing countries. Coronary heart disease caused 17.9 million deaths in 2016-31% of all deaths worldwide. More than three-quarters of deaths in developing countries are caused by heart and vascular diseases. Excessive physical activity, stress, cold weather, unhealthy lifestyle, and family history of heart disease are risk factors for coronary heart disease. The risk of coronary heart disease increases in patients who have many risk factors for heart disease. Therefore, individuals with these risk factors are advised to do early detection with regular heart record checks to prevent and if heart rhythm disturbances are found, patients can undergo further examination based on clinical symptoms and heart record results. The service activity aims to early detection of coronary heart disease through electrocardiogram checks in the academic community of Khairun University. The method used was heart record examination and questioning related to complaints felt when examined. The results obtained from this activity were 14 people who had varying electrocardiogram results. From the activity, it is necessary to periodically monitor the risk of coronary heart disease in order to prevent the occurrence of heart disease. Keywords: Early Detection, Coronary Heart Disease, Electrocardiogram Examination

Aim: Prediction of coronary illness utilizing novel Novel k nearest neighbor (KNN) and contrasting its accuracy with decision tree algorithm. Materials and Methods: Two gatherings are proposed for foreseeing the accuracy (%) of coronary illness. To be specific, novel Novel k nearest neighbor and decision tree algorithm. Here we take 20 examples each for assessment and look at. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result: The decision tree gives better accuracy (84.95%) contrasted with the Novel k nearest neighbor accuracy (76.29 %). Along these lines the factual meaning of the decision tree is superior to the novel k nearest neighbor algorithm with significance value of 0.115. Conclusion: From the outcome, it may very well be inferred that the decision tree helps in anticipating the coronary illness with more precision contrasted with the novel k nearest neighbor algorithm.

Applying data mining techniques to classify patients with suspected hepatitis C virus infection