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.

PurposeTo develop a model for financial forecasting using the principles of qualitative reasoning.Design/methodology/approachThe model was developed using theories in the accounting, finance, and marketing literature. Quantitative equations were transformed into their equivalent qualitative forms. Qualitative equations, where applicable, were developed and integrated with the quantitative equations.FindingsThe research demonstrated that qualitative reasoning models can be used for financial forecasting, decision making and control.Research limitations/implicationsThe model used is experimental and relatively simple. More complex models should be developed for real life applications.Practical implicationsDevelopment of this model demonstrates that more complex models for specific applications in various business domains can be developed and used. Since business decision making requires both qualitative and quantitative inputs, models such as these may be of great practical value.Originality/valueThe paper will be valuable to those who want to develop such models in business domains.

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