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Water Quality (WQ) modeling and forecasting are very challenging for water management bodies due to the complex and nonlinear relationship between the parameters responsible for determining water quality. The main focus of this paper is the water quality prediction of the Ganges River by analyzing the impact of one of the critical configuration parameters of a neural network known as the learning rate. The proposed prediction model based on an artificial neural network (ANN) consists of different sets of experiments performed by comparing twelve different training functions against the variation in learning rates. A total of 360 experiments have been conducted on the dataset collected over the period 2001 to 2015 with five stations along the Ganges River in the state of Uttarakhand, India. All experiments have been conducted in MATLAB software. The ANN-based program is written in Matlab’s NN-Toolbox. As input parameters, we have used temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), and total coliform. The water quality standard set by the Central Pollution Control Board of India has been used. The performance of the developed model has been calculated based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE). Trail training function-based artificial neural network models indicate higher predictive accuracy when compared to other models developed using the remaining eleven training functions when the learning rate is set to 0.04. In conclusion, ANN has the ability to efficiently predict the water quality of rivers and the learning rate has a greater impact on the development of such predictive models. So, it is required to be tuned very carefully.

Analysis of water quality is a very important and challenging task in the management of water bodies and requires immediate attention as it adversely affects the health of living beings. Three parameters namely, pH, Total Dissolved Solids (TDS), and Turbidity were used for data analysis. In this study for mapping of training samples from input space to higher dimensional feature space, LibSVM, (a library of SVM) was used with the use of two kernel function types Radial Basis Function and Polynomial function. For performing the experiment, the three parameter combinations (C, d, ϒ) were evaluated based upon the kernel by taking various range values to obtain the best type of kernel functions through a 10-fold cross-validation process. After performing all experiments, a comparative analysis was done to evaluate the best parameter combination (C, d, ϒ) and the values of performance measures. The result shows that the optimum model developed using LibSVM with the use of Polynomial Kernel function which gives an accuracy of 99.434% in predicting water quality.

Artificial neural networks have progressed in a rapid way in the field of soft computing, and it is widely used in forecasting. The work presented in this chapter is about the development of artificial neural network (ANN)-based models to forecast the water quality (WQ) in Nainital Lake, Uttarakhand, India. A dataset comprising pH, turbidity, and total dissolved solid (TDS) of time period 2018-2019 has been used and analyzed using MATLAB software. For experimentation purposes, four data partition strategies, 10 learning algorithms of back propagation neural network (BPNN), and different combinations of learning rates and training tolerance were evaluated. The performance of the model was evaluated using statistical methods such as MSE, RMSE, MAD, MAPE. The results of the experiment show the capability of the optimal ANN models to predict the WQ of Nainital Lake.

Monitoring of soil elements is essential in characterizing the soil and, therefore, making proper decisions regarding fertilizer application and choice of crops sown. Laboratory soil testing involves the collection of soil samples from different locations and more time and cost. It should upgrade with current technologies like the internet of things (IoT), sensors, and data science. These emerging technologies are smart enough to replace lab testing in real time with minimum effort and with the most accurate results. So, in this research work with a portable IoT device that is coupled with sensors, we can test the soil from wide locations. The system tends to implementation of portable handheld device for soil testing in the Haldwani region. The system is a microcontroller-based device connected to temperature, moisture, and pH sensor. It takes readings from sensors and shows them on LCD. Finally, the data is used for further analysis and comparison. The main idea behind the system is to make it portable to identify the temperature, moisture, and pH of soil.

History should be preserved so that it can be made available for future generations. Thus arises the need for digitization of cultural heritage. The present chapter discusses digitization of cultural heritage of the temples of the state of Uttarakhand, the one which fall in the Kumaon region of North India. Preserving such heritage with the help of digital technology is a fast-emerging trend in the current century. Digital technologies support the recording, analysis, and management of cultural heritage around the world. The chapter brings forth the multiple ways in which it is done and how the same has helped in the preservation, interpretation, and appreciation of the rich cultural heritage. Undoubtedly, they facilitate accessibility and improve understanding of these sites. At the same time, there are fears too in some quarters that digital technologies undermine the need to conserve the “real thing.” The chapter proposes various methods and processes to preserve temples, an important component of cultural heritage.

The big players like Cisco, Hitachi, Siemens, etc. are taking part in designing and developing smart colonies across the globe. The basic applications designed so far in smart cities are for logistic, construction, maintenance, healthcare, and security. One of the prominent issues that is still to be addressed is the management of human resources and robots in smart cities. The proposed application will scan the entire city, based on the available space, green area, and number of human beings, and accordingly, it will suggest the number of buildings, residential areas, malls, parks, free and open space, roads, and number of robots. The chapter will address the basic issues like layout and design of an infrastructure based on human beings and humanoid population. The dos and don'ts for robots will be listed to deal with the aged or young. The additional input based on human being psychology will also be formulated so that robots can handle fragile human beings, especially aged human beings, with care.

Biomedical imaging has gained tremendous attention over the years by aiming to attain better visualization of the abnormalities of structures or disorders in the images of the organs of the body. The motive behind the ensuing procedures of image processing in the medical domain is to improvise the decision-making in the clinical diagnosis and to resolve the issues faced by surgeons to operate the organ by providing the higher dimensional view of internal parts of that organ. The image processing techniques include various steps, including image acquisition, image reconstruction enhancement and analysis. This paper will summarize the imaging (medical) processing techniques used in healthcare informatics along with the challenges faced by the image construal with diverse algorithms, such as image segmentation by edge detection method, k-means etc. The interpretation formed by the physicians after visualizing medical images will improve remote healthcare and help in the treatment of patients in a better manner. The challenges faced by researchers while using a high-technology medical probe to generate better visualization promote their interest in the medical field domain.

Objective: The world of Artificial Intelligence is enormous and so is the technological intervention in making a sophistical machine. The machine is professing about sentiments, beliefs and emotion. The Robots had outclassed human being in terms of knowledge, performance and now in the soft world of emotions. The Industry 4.0 is the world of cyber physical world where everything is likely to revolve around the Machine and cyber space. The sustenance of human being into this sophisticated cyber world is questionable. We have no other option than to evolve more smart, competitive and efficient human being.The research talks about “Articulated Human Intelligence”. Methods/Analysis: Research study deals with a framework of Articulated Human Intelligence. Findings: This research study is proposinga new version of A.I, Internet of things, Big Data analytics, Additive manufacturing in human perspective. Novelty/Improvement: The proposed work will help to redefine Artificial intelligence as Articulated Human Intelligence. Keywords: Articulated Human Intelligence, Big Data Analytics, Industry 4.0, IoT, Worker 4.0

Thoracic diseases are the most common radiological disorders worldwide especially in India. It is a life-threatening infectious disease affecting breathing organ like thorax and one or both lungs in human body commonly caused by bacteria. Physicians and radiologists are quiet using physical and visual graphical manners in order to diagnose the chest X-rays. Patient’s diagnoses are entirely dependent on the consultant given by that chest expert. However, there might be emergency circumstances where radiology experts are too busy or may not be accessible. The timely and early diagnosis of thoracic diseases is very important. To resolve this situation, an algorithm that accept poster anterior (PA) chest X-rays images which classify whether the thorax is infected or not. If a thorax is infected, the proposed model will figure out which type of thoracic disorder is available on that PA view X-ray image. The proposed model can significantly improve the efficiency of doctors by early detection of the diseases using Computer aided diagnosis (CAD) wielding deep learning. Thus, an intelligent and automatic system is required to diagnose the chest radiograph to detect the various thorax related diseases. This research employ a web oriented identification system using deep learning based convolutional neural network algorithms for the detection, classification and early stage diagnosis of chest radiograph into healthy and thoracic disorders patients including COVID-19. The deep learning model is trained and tested on different radiographs which contain normal and numerous thorax disorders patient. Moreover, after developing the neural network model for the early diagnosis of the numerous thoracic disorders, a graphical user interface (web) based disease screening system also described for visualized the accurate diagnosis X-ray images in respective target disease classes.

Cardiovascular health issues are increasing at an alarming rate day by day. This is due to tremendously decreased quality of cardiac health. It is affecting lives of both rural and urban population. Cardiovascular Diseases (CVD) and Coronary Artery Disease (CAD) which previously were considered as the old aged ailments are now significantly evident on all the age groups among rural and urban residents both. The research work majorly focuses on analyzing cardiovascular health of rural and urban residents for early prediction of cardiac ailments through calcium score health indicator. Coronary Angiography is performed and Patients’ Calcium Score results are taken randomly. Calcium score is also termed as Coronary Artery Calcium (CAC). This score is analyzed sex and age-wise in order to predict cardiovascular health issues at early stage. It is evident from the research study that males are affected more than twice of females by the cardiac health issues. The paper tries to figure out various factors affecting cardiac health among rural and urban residents of different age groups. The research outcomes motivates both rural and urban residents towards following a healthy routine and lifestyle in order to avoid such severity of cardiac health issues in future.

Modern healthcare has improved a lot in terms of providing quality treatment to the patients by giving patient-centric care to them and making them aware of the relevant tools available for their self-care. However, these healthcare solutions have not proven sufficient to control the ongoing increase in the rise of diabetic patients. Diabetes is a fast growing chronic disease prevailing in most of the countries that if not dealt carefully will slowly leads to death. Diabetic treatments available today only help patients in controlling it to some extent, but science is still striving to find a proper solution to prevent and cure it. Despite of the advances made in providing better care to patients, there is still an immense scope for treating diabetes in a digital way by embedding it with latest technologies. This paper also proposes a framework, which uses machine learning and precision medicine approaches of bioscience that helps in early diagnoses of disease and provides personalized treatment to the patients.

The paper analyzes the Coronary Artery Calcium (CAC) score metric with respect to the age and gender of the individual. CAC score fairly predicts the risk of cardiovascular diseases. As there is an upsurge in the number of heart patients every day, cardiac health of a person should never be neglected. The Calcium Score involves a non-invasive CT scan of the heart and keeps track of the amount of calcified plaque in coronary arteries. The paper presents statistical comparisons among the scores obtained for males and females across different age groups. These findings are beneficial to predict early Cardiovascular Diseases and surely be useful as an awareness to maintain a healthy lifestyle.

This paper presents a systematic proposal of steps or to model a framework to avoid SCA or SCD at early stage. Nowadays it is being observed that there are so many cases which consult the clinicians or medical practitioners when it is too late or on such state where health issues cannot be addressed towards further improvement or cure. Cardiovascular disease (CVD) or health issues are rapidly increasing day-by-day. So, there is utmost need of a model/framework to avoid increasing cardiovascular health issues at early stage so as the health ailments are successfully avoided or cured. Early stage identification of the cardiac arrest symptoms shall certainly be very beneficial towards our youth as well. CVD cases are too filtered in order to get the chunked sudden cardiac arrest (SCA) cases. SCA cases are in turn considered and measures are thereby taken to avoid sudden cardiac death (SCD). This paper emphasizes on various factors and parameters deciding the health of the human heart including strong family history of CVD or SCD, physical activity, proper nutrition, balanced diet, early follow-up of the health of human heart, etc., in order to avoid heart disease risks at early stages.

The River Ganga (2,525 km long) is the largest River basin in India, covering 26.2 percent of India's total geographical area and recently granted living entity status by the court. It is the holiest River and also among the dirtiest in the world. That’s why it is mandatory to maintain its water quality (WQ). Though, monitoring and assessment of WQ of a River is a very challenging task. In this research work, Soft Computing (SC) based popular and commononly used Artificial Neural Network (ANN) technique has been used for modelling the WQ of the Ganga River by developing a prediction model based on six different training functions. Five sampling stations along this River stretch were selected from DEVPRAYAG to ROORKEE in the Uttarakhand state of India. The monthly data sets of five water quality parameters temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD) and total coliform (TC) for the time period from 2001 to 2015 have been taken. The feed forward error back propagation neural network method has been used to develop the WQ-prediction model by conducting various experiments following a neural network structure of 5-10-1, 0.1 as a training goal and various training functions. Using the Mean square error (MSE) statistical method the prediction performance of the developed model was evaluated. The model developed with traincgp (Conjugate Gradient with Polak-Ribiere Restarts) comes out to be the worst one (MSE=0.786) while the other model with trainlm (Levenberg-Marquardt backpropagation) rule proved to be the best one (MSE=0.163) among others. Consequently, it is found that ANNs are capable of predicting WQ of the River Ganga with acceptable results.

The aim of this paper is to develop automated Internet of Things (IoT) based Water Quality monitoring System. The different sensors are used to monitor the different parameters of water. The whole System having Arduino to interface the sensors and GSM module for remotely monitoring the data. This complete system is solar powered. The device is useful for accessing the quality of water. It can monitor the different water bodies in real time. We are presenting a design and development of a cost effective system for real time water monitoring. Also the device can measure other physical and chemical properties of the water. The Apparatus consist of modules Turbidity Sensor, pH sensor, Temperature & TDS Sensor. All sensors are connected with Ardurino. Ardurino converts the signal form into system readable form and send it to GSM module. The GSM Module will send the sensed data to Smart devices/ cloud through IOT platform. The result can be accessed daily/weekly or monthly.

Diabetes mellitus is one of the severe diseases prevailing in developing countries like India. High level of sugar in blood and urine leads to diabetes which damages the kidneys, eyes, nerves, blood vessels, heart with potential risk of life threat if not cured in time. Diabetes can be prevented with physical exercises, work out, and healthy diet. Diabetes can also be cured if detected in early stages. However, in today's busy life people tend to ignore physical activity and take unhealthy food causing high glucose level in blood. Patients are generally diagnosed with diabetes in late stages due to which it becomes challenging to cure the disease. To prevent and detect the diabetes in early stages, a solution is needed which can integrated with user's day-to-day life and can continuously monitor their diet/fold and physical activities. In this paper, we are proposing cloud-based recommender system/engine which will further integrate with user's smartphone and wearable devices to prevent diabetes by recommending healthy lifestyle and diet to diabetic patients.

Cognitive Radio Networks (CRNs) is promising solution to spectrum scarcity which can enhance utilization of unused bandwidth in future networks, it indicate that CRNs are familiar of context and are capable of finding unused bandwidths of licensed user or Primary User (PU) and use them for transmission by unlicensed users or Secondary User (SU). The SU must release the bandwidth when PU has to access the same bandwidth without any delay and SU might be diverted to some other route. We propose an algorithm known as the ant colony optimization (ACO) for providing effective routing to SU, it is inspired by real ant behavior which is optimized for finding good routes through graphs where multiple route are required for packet transmission resulting in an enhanced throughput. The key objective is to provide alternative path to SU while PU acquires the previous path of SU during packet transmission in an efficient manner. Simulations results using NS 2 of AODV protocol are presented to indicate the performance of the proposed solution.

Predicting the water quality of rivers has attracted a lot of researchers all around the globe. A precise prediction of river water quality may benefit the water management bodies. However, due to the complex relationship existing among various factors, the prediction is a challenging job. Here, the authors attempted to develop a model for forecasting or predicting the water quality of the river Ganga using application of predictive intelligence based on machine learning approach called support vector machine (SVM). The monthly data sets of five water quality parameters from 2001 to 2015 were taken from five sampling stations from Devprayag to Roorkee in the Uttarakhand state of India. The experiments are conducted in Python 2.7.13 language (Anaconda2 4.3.1) using the radial basis function (RBF) as a kernel for developing the non-linear SVM-based classifier as a model for water quality prediction. The results indicated a prediction performance of 96.66% for best parameter combination which proved the significance of predictive intelligence in water quality forecasting.

This paper explains a new feature extraction image pre-processing system followed by back propagation- artificial neural networks based system for class categorization of apple fruit images. Scale Conjugate Gradient (SCG) algorithm is used for back propagation. The methodology comprises of three stages in this work. Firstly, various external image based attributes of apple were taken and process in MATLAB. Size and weight features were also considered as important parameters as only color is not sufficient to judge the quality. Secondly, features extraction was done at image pre-processing for making the algorithm lighter by focusing only key features. Support vector machine (SVM) algorithm is also popular for development of relatively light weight classification models. This work uses artificial neural network toolbox in MATLAB for classification. A single hidden layer BP-ANN (Back propagation- artificial neural network) was used with sigmoid activation functions. The result came in terms of suitable output variable which is the quality class of the apple which is chosen A, B, C and D respectively. The modeling result indicates the tremendous match between the data used in training and assumed output values. It also has shorter calculation time due to the SCG algorithm. It is also possible for apple producers and distributors to classify their fruit using this model and reduce the cost by avoiding manual classification.

Obtaining thematic maps using image classification techniques from hyperspectral datasets is a very difficult image processing task. In hyperspectral image analysis dimensionality reduction is one of the challenging pre-processing tasks, which is achieved using feature extraction techniques. The beauty of these techniques is that they drastically reduces the dimensionality of image and at the same time preserves the majority of the essential information. In this paper few most frequently used dimensionality reduction methods are being investigated, which helps to get accurateness. This research work presents a relative performance investigation of few mostly frequently used feature extraction techniques like Decision Boundary Feature Extraction (DBFE), Non-Parametric Weighted Feature Extraction (NWFE), Discriminative analysis feature extraction (DAFE) and Principal Component Analysis (PCA). The classification is carried out using two most widely used classification techniques including Gaussian Maximum Likelihood (GML) and neural network (NNs). The results obtained after performing experiments indicates that Decision Boundary Feature Extraction (DBFE) technique has provided the best accuracy among various investigated feature extraction techniques. The application areas of this research include areas like identification of exact location in battle field, drought affected areas, flooded areas and weather forecasting etc.

Now days in area of computer science Green computing is creating revolution by bringing some new digital component with less power consumption. Our research work is created on this idea. In this paper our objective is to come up with High Performance RAM design for IOT based processor by reducing the power consumption. For calculating total power consumption of FPGA based RAM we used five WLAN frequencies (900 MHz, 2.4GHz, 3.6GHz, 5GHz, 5.6GHz) and calculated Clock, Logic, Signals, IOs, Leakage power at four different IOs standard (SSTL 135, SSTL15, SSTL18_I, SSTL18_II). In experiment we found that if we will use the SSTL135 instead of SSTL18_II IO standard then we can reduced power consumption by 8.69% at 900MHz, 10.74% at 2.4MHz, 11.80% at 3.6MHz, 12.86% at 5MHz and 13.29% at 5.9MHz. We think that the application of this design will definitely help to design in futuristic IOT based processor development.

In the field of Healthcare Information Technology telemedicine is playing crucial role by providing medical facility to distant zones. To be better utilization of telemedicine in rural area Indian Space and Research Organization (ISRO) has setup rural community supply centers handled by Non-government Organizations (NGO‘s). In this paper we basically discussed the Uttrakhand rural areas because Himalayan Institute Hospital Trust presented initial health treatment via tele-meeting to inaccessible tribal hilly zones through village reserve points. Eventually our paper provides the technological and fiscal hurdles faced in implementing tele-consultation.

Health recommender system (HRS) has been quite useful in providing effective recommendations not only to the patients but also to the physicians. However, this paper focusses on the patient-centric model and our major concern will be towards the recommendations that will serve as a boon to the patients by recommending suitable treatments to them based on analysis of other patient’s profile with similar attributes. Patients have to provide their contextual information such as diagnosis and symptoms which will be maintained in the form of personal health records (PHR). HRS using patient-centric healthcare model as proposed in this paper will not only help the patients in the early prediction and treatment of disease, but also help in reducing the costs of treatments. Model will also be leveraged by patients in selection of healthcare provider based on successful treatment delivered by them to patients with similar profiles and symptoms.

In this paper the classification of hyperspectral images is investigated by using a supervised approach. The spectral feature are extracted with well known decision boundary feature extraction (DBFE) and non-parametric weighted feature extraction (NWFE) techniques. The most informative features are fed to random forest (RF) classifier to perform pixel-wise classification. The experiments are carried out on two benchmark hyperspectral images. The results show that RF classifier generates good classification accuracies for hyperspectral image with smaller execution time. Among feature extraction techniques, DBFE has produced better results than NWFE.

Recently, Water Quality (WQ) comes out to be the central point of concern all around the globe. The purpose of this work is to develop an automated procedure that can be used to classify the water quality of the River Ganga proficiently in the stretch from Devprayag to Roorkee Uttarakhand, India. The monthly data sets of five water quality parameters temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD) and total coliform (TC) for the time period from 2001 to 2015 is used for this research work. The proposed method involves developing various water quality classification models using one of the concept of data mining called decision tree (DT) for evaluating the WQ classes. The experiments are conducted using Weka data mining tool. Models first developed using (60–40)% data division approach and then using (80–20)% approach of data division. Five different decision tree models are developed named J48 (C4.5), Random Forest, Random Tree, LMT (logistic model tree) and Hoeffding Tree. These classifiers were analyzed to determine the most accurate classifier model for the present dataset by evaluating their performance via measures like Accuracy, Kappa Statistics, Recall, Precision, F-Measure, Mean absolute error and Root mean squared error. The results concluded that the random forest model outperforms all other classifiers with a great accuracy rate of 100% in both approaches and least error rate when developed using the second approach. Such a highly acceptable and attractive results may be helpful for the decision makers in water management and planning.

Contrast enhancement in color images rises the contrast of a colored image to improve its quality. The unwanted information gets added into the captured image due the restricted capacities of the device used for capturing images and the atmospheric changes. This results in blurring of images. So the captured image suffers from information losses which reduce its quality. Thus image enhancement techniques play a significant role in recovering the useful data from the image. The paper represents a review analysis on few the studies based on image enhancement methods for improving images qualities corresponding to Spatial Domain, Histogram Processing, Contrast Stretching, Histogram Equalization etc.

There has been high influx of data over the Web in the past few decades. In order to convert this data into meaningful information, data analytics platforms such as big data analytics have emerged. Recommendation system (RS) is one such analytic system which uses predictive analysis to present and target the useful information to the users in order to help them arriving at desired conclusions. RS saves the time of most of the Internet users by reducing the number of actions needed to get to the required information which helps in effective decision making. RS has been widely used in multiple domains such as e-commerce, social networks to present recommended options to users in their current context. Recommended options thereby presented are based on the action taken by other users in similar context. RS is equally useful in healthcare services. In fact, they are a complementary tool to healthcare system which allows for effective decision making in healthcare services. RS uses either collaborative filtering approach, content filtering approach, or a blend of them (hybrid approach). This paper discusses the overview of what recommender systems are, how they are built, and its classifications. It also elaborates health recommender system (HRS) and gives a clear picture of how MapReduce Framework and Hadoop technology will help in improving the scalability and efficiency of HRS by stating illustrations.

The Healthcare industry has grown tremendously in recent years providing best possible medical facility in such an effective manner, i.e., in terms of time as well as cost. Healthcare industry needs to define the standard and need to bring the analytical approach to next level. In this Paper, we have gone through various researchers ideas that represent their approach to effectively provide the solutions regarding prediction of various cardiovascular health issues at multiple levels. Predictive analytics differ from the descriptive and prescriptive analytics by utilizing patient’s medical records and analyze them with various statistical techniques as well as advanced machine learning algorithms. This paper will present recent research using various predictive analytical tools and techniques in order to analyze the cardiovascular health issues and in predicting the future outcome of the analysis with much efficient and effective way. There is a need to analyze and examine possible future work to have a better understanding of applying more hybrid and effective algorithms.

The Ganga River is the national river of India and recently got the right of living entity. It is necessary to monitor its water quality frequently. But monitoring and assessment of a river WQ is a provocative task in ecological management. In this study, soft computing based one of the computation technique called Artificial Neural Network (ANN) is used to developpredictionor forecasting modelfor estimating the WQ of the river Ganga in the stretch from Devprayag to Roorkee, Uttarakhand, India. Five sampling stations along this river stretch areselected. The monthly data sets of five water quality parameters temperature, pH, dissolved oxygen, biochemical oxygen demand and total coliform is usedfrom year 2001 to 2015. In developing the ANN model for the WQ prediction, the feed forward error back propagation method is used to develop predictionmodel for conducting various experimental investigationsfollowing many training parameters by adopting the neural network configurationof 5-10-1. The prediction performance of the developed model is evaluated using one of the statistical means named mean square error. The predicted WQ model resulted in a higher accuracy by producing best MSEvalue of 0.041 when using trainlm(Levenberg-Marquardt backpropagation)as a training function. The present study concluded that the ANNs are capable of predicting WQ of the river Ganga with acceptable accuracy of 95.9%.

This paper presents the experimental analysis of data provided by UCI machine learning repository. Weka open source machine learning tool provided by Waikato University reveals the hidden fact behind the datasets on applying supervised mathematical proven algorithm, i.e., J48 and Naive Bayes algorithm. J48 is an extension of ID3 algorithm having additional features like continuous attribute value ranges and derivation of rules. The data sets were analyzed using two approaches, i.e., first taken with selected attributes and taken with all attributes. The performance of both the algorithm reveals the accuracy of algorithm and predicting the various reasons behind this increasing problem of cardiovascular diseases.